Análise de ligação e de QTL de uma população de linhagens endogâmicas recombinantes (RIL) originada de uma nested-association mapping (NAM) de milho
Helio de Souza Junior 1
2022-06-14
Este projeto foi proposto na disciplina FIT 678 - Análise de dados genéticos no melhoramento de plantas da UFV, ministrada pelo Prof. Guilherme da Silva Pereira.
O propósito desse projeto foi realizar análises de ligação e de QTL em um conjunto de dados reais. O objetivo é realizar uma Análise de ligação I: segregação de marcadores, cálculo da fração de recombinação dois-pontos, e formação dos grupos de ligação; Análise de ligação II: ordenação dos marcadores; Análise de QTL I: análise de marcas individuais; Análise de QTL II: mapeamento por intervalo; Análise de QTL III: mapeamento por intervalo composto e Análise de QTL IV: mapeamento por múltiplos intervalos.
DADOS DO MILHO
Trata de uma população RIL com 1106 marcadores em 185 indivíduos e foram avaliados 29 fenótipos que consistem na característica da altura da planta.
library(qtl)population_Z006 <- read.cross(format="csv", file="population_z006.csv", genotypes=c("0","1","2"), crosstype = "riself")## --Read the following data:
## 185 individuals
## 1106 markers
## 29 phenotypes
## --Cross type: riselfsummary(population_Z006)## RI strains via selfing
##
## No. individuals: 185
##
## No. phenotypes: 29
## Percent phenotyped: 100 100 100 100 100 100 100 100 100 100 100 100 100 100
## 100 100 100 100 100 100 100 100 100 100 100 100 100 100
## 100
##
## No. chromosomes: 1
## Autosomes: 0
##
## Total markers: 1106
## No. markers: 1106
## Percent genotyped: 92.1
## Genotypes (%): AA:48.4 BB:51.6geno.image(population_Z006)
gt.population_z006 <- geno.table(population_Z006)
gt.population_z006## chr missing AA BB P.value
## PZA02255.2 0 10 87 88 9.397430e-01
## PZA00240.6 0 22 85 78 5.834981e-01
## PZA01879.1 0 16 80 89 4.887441e-01
## PZB02155.1 0 9 83 93 4.509823e-01
## PZA02527.2 0 2 99 84 2.675027e-01
## PZA03742.1 0 12 91 82 4.938127e-01
## PZA00521.3 0 12 107 66 1.825948e-03
## PZB02002.1 0 5 86 94 5.509850e-01
## PZA02247.1 0 19 82 84 8.766399e-01
## PZA03529.1 0 9 84 92 5.464936e-01
## ae1.8.7 0 15 70 100 2.139757e-02
## PZA03212.3 0 12 93 80 3.229706e-01
## PZA02941.7 0 18 82 85 8.164239e-01
## PZA02191.1 0 25 88 72 2.059032e-01
## PZA03699.1 0 13 65 107 1.362545e-03
## PZA03728.1 0 11 82 92 4.483923e-01
## PHM4341.42 0 9 85 91 6.510766e-01
## PZA01537.2 0 12 87 86 9.393964e-01
## PZA03227.1 0 2 87 96 5.058592e-01
## PZA02272.3 0 13 68 104 6.051564e-03
## PZA02402.1 0 11 80 94 2.885367e-01
## PZA02454.2 0 11 85 89 7.617076e-01
## PZA01035.1 0 15 82 88 6.453877e-01
## PZA02208.1 0 12 74 99 5.733938e-02
## PZA01044.1 0 9 91 85 6.510766e-01
## PHM532.23 0 16 71 98 3.780866e-02
## PZA02418.2 0 3 82 100 1.821223e-01
## PZA01369.1 0 4 89 92 8.235447e-01
## PZA00225.8 0 13 82 90 5.418656e-01
## PZA03634.1 0 14 80 91 4.002409e-01
## lac1.3 0 13 81 91 4.457659e-01
## PZA01875.1 0 6 85 94 5.011435e-01
## PZA02513.1 0 12 62 111 1.950050e-04
## PZA00616.13 0 8 80 97 2.013206e-01
## PZA01954.1 0 33 88 64 5.157586e-02
## PZA03305.7.1 0 17 88 80 5.370940e-01
## PZA01658.1 0 12 89 84 7.038393e-01
## PHM4786.9 0 51 64 70 6.042343e-01
## PZA00760.1 0 7 86 92 6.529131e-01
## PZA01332.2 0 11 99 75 6.884504e-02
## PZA03469.1 0 11 83 91 5.441971e-01
## PZA01623.3 0 6 84 95 4.109753e-01
## PZA02654.3 0 7 76 102 5.132142e-02
## PZA00355.2 0 8 86 91 7.070485e-01
## PZA02792.26.25 0 13 82 90 5.418656e-01
## PZB00761.1 0 11 81 93 3.629714e-01
## PHM7922.8 0 12 85 88 8.195796e-01
## PZA00706.16 0 22 81 82 9.375687e-01
## PZA00589.10 0 11 83 91 5.441971e-01
## PZA01278.2 0 13 90 82 5.418656e-01
## PZA02087.2 0 9 91 85 6.510766e-01
## sh1.12.11 0 9 101 75 5.001640e-02
## PZA02252.2 0 6 86 93 6.008319e-01
## PZA02457.1 0 7 67 111 9.739714e-04
## PZA03166.1 0 10 82 93 4.056789e-01
## PZA01726.1 0 6 82 97 2.622229e-01
## PZA02359.10 0 14 92 79 3.201572e-01
## PZA02117.1 0 5 78 102 7.363827e-02
## zb27.1 0 14 81 90 4.912971e-01
## PZA02113.1 0 10 84 91 5.967012e-01
## PZA02688.2 0 12 85 88 8.195796e-01
## PZB00079.4 0 13 91 81 4.457659e-01
## PZA02128.3 0 7 91 87 7.643200e-01
## PZA01759.1 0 10 62 113 1.156173e-04
## PZA01154.1 0 26 76 83 5.788016e-01
## PZA02291.1 0 13 83 89 6.473148e-01
## PZA00193.2 0 9 103 73 2.373852e-02
## PHM904.21 0 13 83 89 6.473148e-01
## PZA03488.1 0 16 83 86 8.174941e-01
## PZA00680.3 0 4 75 106 2.121075e-02
## PHM14055.6 0 10 62 113 1.156173e-04
## PZA01316.1 0 9 82 94 3.657123e-01
## PZA03116.1 0 32 70 83 2.932642e-01
## PZA03037.2 0 14 87 84 8.185458e-01
## PZA00838.2 0 27 78 80 8.735811e-01
## PHM13020.10 0 14 83 88 7.021947e-01
## PHM1968.22 0 11 97 77 1.294698e-01
## PZA02325.4 0 7 84 94 4.535368e-01
## PHM3342.31 0 26 76 83 5.788016e-01
## PZA02011.1 0 10 88 87 9.397430e-01
## PHM5526.25 0 9 94 82 3.657123e-01
## PZA01050.1 0 22 71 92 1.000014e-01
## PZB01235.4 0 12 99 74 5.733938e-02
## PZA00498.5 0 20 86 79 5.857884e-01
## PZA03457.1 0 5 96 84 3.710934e-01
## PZA01367.2 0 12 103 70 1.210928e-02
## PZA01796.1 0 16 72 97 5.447039e-02
## PZB01403.1 0 13 86 86 1.000000e+00
## PZA01348.1 0 26 77 82 6.917172e-01
## PHM10404.8 0 22 88 75 3.085646e-01
## PZA03024.16 0 13 69 103 9.528791e-03
## PZA00323.3 0 8 85 92 5.987824e-01
## PZA01866.1 0 21 79 85 6.394119e-01
## PZA01618.2 0 16 80 89 4.887441e-01
## PZA01995.2 0 12 82 91 4.938127e-01
## PHM3078.12 0 24 75 86 3.859851e-01
## PZA00948.1 0 4 76 105 3.111858e-02
## PZB00765.1 0 13 62 110 2.522490e-04
## PZA00675.1 0 6 88 91 8.225779e-01
## PZA02236.1 0 12 87 86 9.393964e-01
## PZA01563.1 0 33 68 84 1.943659e-01
## PHM6111.5 0 20 77 88 3.918049e-01
## PZA02961.6 0 11 83 91 5.441971e-01
## PZA02393.2 0 14 86 85 9.390437e-01
## PZA00084.2 0 12 86 87 9.393964e-01
## PZB01021.1 0 11 100 74 4.871760e-02
## PZA02577.1 0 17 90 78 3.545395e-01
## PZA02367.1 0 4 105 76 3.111858e-02
## PZA02386.2 0 43 72 70 8.667121e-01
## PHM1870.20 0 18 74 93 1.414902e-01
## PZA02129.1 0 6 96 83 3.312169e-01
## PZA02480.1 0 1 74 110 7.955439e-03
## PZA01280.2 0 11 88 86 8.794870e-01
## PZA02678.1 0 15 91 79 3.573857e-01
## PZA03747.1 0 13 65 107 1.362545e-03
## PZB01662.1 0 10 88 87 9.397430e-01
## PZA02955.3 0 9 77 99 9.725443e-02
## PZA00068.1 0 10 98 77 1.124106e-01
## PZA00436.7 0 10 73 102 2.836551e-02
## PHM4503.25 0 15 86 84 8.780884e-01
## PHM1307.11 0 12 60 113 5.589196e-05
## PZA03275.4.1 0 8 92 85 5.987824e-01
## PHM18705.23 0 13 85 87 8.787937e-01
## PZA00508.2 0 32 80 73 5.714506e-01
## PHM7616.35 0 4 95 86 5.035180e-01
## glb1.2 0 29 79 77 8.727801e-01
## PZA01993.7 0 9 92 84 5.464936e-01
## PZA00455.14.16 0 12 94 79 2.541077e-01
## PZB00942.1 0 15 83 87 7.590063e-01
## PZA02289.2 0 11 95 79 2.251463e-01
## PZA03577.1 0 5 95 85 4.560565e-01
## PHM816.29 0 6 87 92 7.086145e-01
## PZA00163.4 0 4 88 93 7.101556e-01
## PHM4353.31 0 13 83 89 6.473148e-01
## PZA01691.1 0 11 73 101 3.378114e-02
## PHM6428.11 0 21 82 82 1.000000e+00
## PZA02779.1 0 11 99 75 6.884504e-02
## PHM5484.22 0 9 79 97 1.748444e-01
## PZA01638.1 0 21 83 81 8.758961e-01
## PZA01241.2 0 10 86 89 8.205958e-01
## PHM1959.26 0 13 80 92 3.601961e-01
## PZA02948.24 0 9 89 87 8.801685e-01
## PHM3330.25 0 10 86 89 8.205958e-01
## PZA01238.1.2 0 11 94 80 2.885367e-01
## PZA03070.9 0 13 86 86 1.000000e+00
## PZA02390.1 0 51 35 99 3.225060e-08
## PZA01055.1 0 13 81 91 4.457659e-01
## PZA02081.1 0 21 67 97 1.914957e-02
## PZA00175.2 0 23 77 85 5.296507e-01
## PZA00158.2 0 8 88 89 9.400837e-01
## PZA03717.1 0 21 69 95 4.233023e-02
## PHM9418.11 0 24 89 72 1.803144e-01
## PZA01744.1 0 48 72 65 5.498063e-01
## PZA01141.1 0 9 84 92 5.464936e-01
## PZA00243.25 0 8 94 83 4.083444e-01
## PHM1184.26 0 2 87 96 5.058592e-01
## PHM2770.19 0 18 82 85 8.164239e-01
## PHM10225.15 0 11 90 84 6.492108e-01
## PZA00793.2 0 14 91 80 4.002409e-01
## PZA02068.1 0 66 22 97 6.188613e-12
## PZA03677.1 0 22 71 92 1.000014e-01
## PZA02164.16 0 22 71 92 1.000014e-01
## PZA01936.4 0 11 86 88 8.794870e-01
## zb7.2 0 26 84 75 4.753840e-01
## PZA00425.11 0 33 77 75 8.711315e-01
## PZA03629.1 0 13 94 78 2.224692e-01
## PZA00708.3 0 6 88 91 8.225779e-01
## PHM14104.23 0 25 78 82 7.518296e-01
## PZA01978.23 0 9 92 84 5.464936e-01
## PZA00079.1 0 12 83 90 5.945874e-01
## PZA01901.1 0 10 88 87 9.397430e-01
## PHM1218.6 0 19 95 71 6.249586e-02
## PZA03255.1 0 19 80 86 6.414372e-01
## PZA00106.10 0 6 88 91 8.225779e-01
## PZA00704.1 0 12 73 100 4.009470e-02
## PZA03605.1 0 11 83 91 5.441971e-01
## PZA01962.12 0 7 76 102 5.132142e-02
## PZA00682.17.2 0 10 90 85 7.054570e-01
## PZA03182.5 0 17 84 84 1.000000e+00
## PHM3852.23 0 15 83 87 7.590063e-01
## PZA00804.1 0 4 82 99 2.063736e-01
## PZA02514.1 0 10 85 90 7.054570e-01
## PZA03521.1 0 6 88 91 8.225779e-01
## PZA01038.1 0 9 84 92 5.464936e-01
## PZA02668.2 0 11 86 88 8.794870e-01
## PHM13673.53 0 18 80 87 5.880415e-01
## bt2.7.4 0 10 60 115 3.215956e-05
## PZA02890.4 0 28 75 82 5.763932e-01
## PZA01933.3 0 19 83 83 1.000000e+00
## PZA02002.1 0 11 66 108 1.452491e-03
## PZA01195.3 0 9 101 75 5.001640e-02
## PZA00214.1 0 50 67 68 9.314137e-01
## PZA01597.1 0 14 84 87 8.185458e-01
## PZA02274.1 0 5 93 87 6.547208e-01
## PZA01073.1 0 10 90 85 7.054570e-01
## PZA02235.14 0 25 75 85 4.291953e-01
## PZA00222.7 0 14 76 95 1.462331e-01
## PZA01445.1 0 12 84 89 7.038393e-01
## PZA01360.3 0 14 84 87 8.185458e-01
## PZA03155.3 0 15 100 70 2.139757e-02
## PZA00118.1.5 0 9 85 91 6.510766e-01
## PZA02168.1 0 12 95 78 1.961889e-01
## PZA00511.3 0 22 80 83 8.142257e-01
## PZA00058.1 0 23 77 85 5.296507e-01
## PZA01693.1 0 12 77 96 1.485862e-01
## PZA01294.2.1 0 14 72 99 3.894746e-02
## PZA03391.1 0 12 86 87 9.393964e-01
## PZA00307.14 0 30 83 72 3.769439e-01
## PZA00525.17 0 10 71 104 1.261114e-02
## PZA00223.4 0 17 87 81 6.434288e-01
## PZA01909.1.2 0 7 82 96 2.940197e-01
## PZA00453.2 0 6 85 94 5.011435e-01
## PZA01289.1 0 8 88 89 9.400837e-01
## PZA02479.1 0 11 100 74 4.871760e-02
## PHM6238.36 0 7 88 90 8.808385e-01
## PZA02578.1 0 10 89 86 8.205958e-01
## PZA00416.7 0 7 87 91 7.643200e-01
## PHM824.17 0 16 81 88 5.902585e-01
## PZA01527.1 0 10 86 89 8.205958e-01
## PZB00232.2 0 16 76 93 1.909777e-01
## PZA01386.3 0 20 94 71 7.336593e-02
## PZA02470.2 0 23 78 84 6.373519e-01
## PZA03198.3 0 6 88 91 8.225779e-01
## PZA00694.6 0 11 106 68 3.967018e-03
## PZA03172.3 0 17 70 98 3.075356e-02
## PZA03320.6 0 18 69 98 2.482678e-02
## PZA01753.1 0 13 72 100 3.276265e-02
## PZB01457.1 0 34 70 81 3.706977e-01
## PHM1275.22 0 16 92 77 2.485632e-01
## PZA02741.1 0 34 84 67 1.665299e-01
## PHM14152.18 0 7 87 91 7.643200e-01
## PHM11985.27 0 12 84 89 7.038393e-01
## zb21.1 0 11 86 88 8.794870e-01
## kip1.3 0 18 81 86 6.988216e-01
## PZA00265.6 0 12 89 84 7.038393e-01
## PZA03317.1 0 17 70 98 3.075356e-02
## PZA03650.1 0 22 81 82 9.375687e-01
## PZA00409.17 0 13 84 88 7.603683e-01
## PHM3626.3 0 25 79 81 8.743671e-01
## PZA03165.1 0 21 78 86 5.321712e-01
## PZA02260.2 0 4 95 86 5.035180e-01
## PZA00402.1 0 12 85 88 8.195796e-01
## PZA03193.2 0 9 85 91 6.510766e-01
## PHM11114.7 0 10 92 83 4.962917e-01
## PZA00494.2 0 24 76 85 4.781387e-01
## PZA02151.3 0 12 106 67 3.025697e-03
## PZA01533.2 0 16 87 82 7.005224e-01
## PZA02663.1 0 9 83 93 4.509823e-01
## PHM2100.21 0 10 98 77 1.124106e-01
## an1.5 0 14 96 75 1.082937e-01
## PZA03142.5 0 12 85 88 8.195796e-01
## PZA01365.1 0 11 76 98 9.535232e-02
## PZA01790.1 0 10 99 76 8.209871e-02
## PZA02699.1 0 14 88 83 7.021947e-01
## PZA00100.10 0 15 92 78 2.829343e-01
## PZA01267.3 0 16 96 73 7.685537e-02
## PZA02645.2 0 12 88 85 8.195796e-01
## PZA00282.19 0 8 92 85 5.987824e-01
## PHM13360.13 0 13 87 85 8.787937e-01
## PZA01902.1 0 13 87 85 8.787937e-01
## PZA00881.1 0 13 76 96 1.272627e-01
## PHM16437.20 0 13 85 87 8.787937e-01
## PZA00186.4 0 15 77 93 2.197685e-01
## PZA02408.2 0 17 70 98 3.075356e-02
## PZA00362.1 0 13 75 97 9.344782e-02
## PZA01509.1 0 10 88 87 9.397430e-01
## PHM13623.14 0 11 62 112 1.503503e-04
## PZA02698.3 0 10 97 78 1.509270e-01
## PZA02316.22 0 18 90 77 3.144299e-01
## PZA02769.1 0 1 74 110 7.955439e-03
## PZA00509.1 0 13 88 84 7.603683e-01
## PZA01963.15 0 32 76 77 9.355651e-01
## PZB01461.1 0 12 103 70 1.210928e-02
## PZA02853.11 0 13 84 88 7.603683e-01
## PZA03069.8.4 0 8 87 90 8.215951e-01
## PZA03573.1 0 1 89 95 6.582534e-01
## PHM15474.5 0 12 88 85 8.195796e-01
## PZA00939.1 0 24 89 72 1.803144e-01
## PZA02862.3 0 13 75 97 9.344782e-02
## PZA02815.25 0 4 88 93 7.101556e-01
## PZA01672.1 0 49 66 70 7.316006e-01
## PHM2828.83 0 64 59 62 7.850629e-01
## PZA01187.1 0 11 92 82 4.483923e-01
## PZA02417.2 0 43 61 81 9.327631e-02
## PZA00752.1 0 16 94 75 1.438677e-01
## PZA03188.3 0 8 90 87 8.215951e-01
## PZA00878.2 0 13 103 69 9.528791e-03
## PHM11226.13 0 7 86 92 6.529131e-01
## PZA00996.1 0 33 68 84 1.943659e-01
## PZA03735.1 0 35 67 83 1.914184e-01
## PHM6043.19 0 12 82 91 4.938127e-01
## PZA00081.18 0 12 94 79 2.541077e-01
## PZA00395.2 0 17 59 109 1.145134e-04
## PZA01391.1 0 32 76 77 9.355651e-01
## PZA00343.31 0 12 95 78 1.961889e-01
## PZB00901.3.4 0 13 68 104 6.051564e-03
## PZA02522.1 0 9 91 85 6.510766e-01
## PHM14412.4 0 21 78 86 5.321712e-01
## PZA00181.2 0 6 88 91 8.225779e-01
## PZA00978.1 0 8 93 84 4.987350e-01
## PZA02299.16 0 5 87 93 6.547208e-01
## PZB00094.1 0 26 83 76 5.788016e-01
## PHM2343.25 0 11 87 87 1.000000e+00
## PHM4913.18 0 24 85 76 4.781387e-01
## PHM3726.129 0 13 89 83 6.473148e-01
## PZA03638.1 0 13 84 88 7.603683e-01
## PZA01779.1 0 13 75 97 9.344782e-02
## PZA02337.4 0 11 75 99 6.884504e-02
## PZA00155.1 0 10 101 74 4.125002e-02
## PZA01552.1 0 16 80 89 4.887441e-01
## PZA01619.1 0 12 85 88 8.195796e-01
## PZA01791.2 0 11 81 93 3.629714e-01
## PZA00424.1 0 48 72 65 5.498063e-01
## PZA03741.1 0 12 80 93 3.229706e-01
## PHM4468.13 0 10 84 91 5.967012e-01
## PZA02175.1 0 10 72 103 1.910992e-02
## PZA03559.1 0 43 61 81 9.327631e-02
## PHM3094.23 0 20 84 81 8.153346e-01
## PZA01232.1 0 13 87 85 8.787937e-01
## PZA00213.19 0 8 85 92 5.987824e-01
## PZA03527.1 0 14 91 80 4.002409e-01
## PZB02122.1 0 8 77 100 8.384743e-02
## PZA02465.1 0 11 90 84 6.492108e-01
## PZA00210.1.9 0 9 88 88 1.000000e+00
## PZA02048.2 0 15 80 90 4.431023e-01
## PZA00887.1 0 17 82 86 7.576207e-01
## PZA01601.1 0 5 89 91 8.814975e-01
## PZB00544.2 0 8 91 86 7.070485e-01
## PHM14046.9 0 8 89 88 9.400837e-01
## PZA01114.2 0 11 86 88 8.794870e-01
## PZA00497.4 0 14 77 94 1.935933e-01
## PHM4125.11 0 10 100 75 5.878172e-02
## PZA01425.2 0 9 85 91 6.510766e-01
## PZA02203.1 0 9 95 81 2.912928e-01
## PZA01135.1 0 14 94 77 1.935933e-01
## PZA03385.1 0 12 61 112 1.055535e-04
## PZA00273.5 0 13 76 96 1.272627e-01
## PZA01530.1 0 33 68 84 1.943659e-01
## PZA01729.1 0 14 83 88 7.021947e-01
## PHM5359.10 0 18 90 77 3.144299e-01
## PZA02017.1 0 9 84 92 5.464936e-01
## PZA01877.2 0 13 86 86 1.000000e+00
## PHM4926.16 0 32 74 79 6.860465e-01
## PZA01919.2 0 9 91 85 6.510766e-01
## PZA02135.2 0 34 84 67 1.665299e-01
## PZA00739.1 0 12 85 88 8.195796e-01
## PZA00647.9 0 12 85 88 8.195796e-01
## PZA01303.1 0 16 73 96 7.685537e-02
## PZA00463.3 0 12 84 89 7.038393e-01
## PZB00752.1 0 9 80 96 2.278000e-01
## PZA02262.3 0 13 81 91 4.457659e-01
## PZA01607.1 0 12 84 89 7.038393e-01
## PZA02564.2 0 4 87 94 6.028504e-01
## PZA02194.1 0 11 95 79 2.251463e-01
## PZA03603.1 0 11 83 91 5.441971e-01
## PZA00541.1 0 10 62 113 1.156173e-04
## PHM3457.6 0 24 82 79 8.130966e-01
## PZA01921.20.19 0 4 96 85 4.135722e-01
## PZA01089.1 0 10 85 90 7.054570e-01
## PZA02141.1 0 9 87 89 8.801685e-01
## PZA02746.2 0 22 75 88 3.085646e-01
## PZA01713.4 0 11 62 112 1.503503e-04
## PHM13823.7 0 11 86 88 8.794870e-01
## PZD00022.5 0 3 95 87 5.531815e-01
## PZA00390.7 0 7 86 92 6.529131e-01
## PZA00513.1 0 11 106 68 3.967018e-03
## PZA02182.1 0 14 83 88 7.021947e-01
## PZA01060.1 0 1 74 110 7.955439e-03
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## PZA00337.4 0 11 87 87 1.000000e+00
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## PHM12992.5 0 33 68 84 1.943659e-01
## PZB00014.1 0 11 83 91 5.441971e-01
## PHM1978.111 0 14 83 88 7.021947e-01
## PZB00959.1 0 11 85 89 7.617076e-01
## PZA01600.2 0 5 90 90 1.000000e+00
## PZD00033.3 0 13 84 88 7.603683e-01
## PZA02278.1 0 11 95 79 2.251463e-01
## PZA01230.1 0 10 86 89 8.205958e-01
## PZA01209.1 0 10 94 81 3.257514e-01
## PZA01216.1 0 5 92 88 7.655945e-01
## PZA01447.1 0 12 88 85 8.195796e-01
## PZB00547.3 0 7 92 86 6.529131e-01
## PZA02467.10 0 8 90 87 8.215951e-01
## PZA00505.6 0 12 89 84 7.038393e-01
## PZB00811.1 0 22 81 82 9.375687e-01
## PZA03176.4 0 6 91 88 8.225779e-01
## PHM15331.16 0 9 88 88 1.000000e+00
## PZA02748.3 0 13 84 88 7.603683e-01table(gt.population_z006$P.value < 0.05)##
## FALSE TRUE
## 947 159table(gt.population_z006$P.value < 0.05/totmar(population_Z006))##
## FALSE TRUE
## 1091 15Construção do mapa de ligação
Carregar dados e estimar frações de recombinação em pares
Para começar com a análise, carregamos os dados (aqui, estamos usando a população de linha de raça recombinante Z006) usando a função , e estimamos as frações de recombinação de pairwise (e seus respectivos escores lod) usando a função :read.cross()est.rf()
population_Z006 <- read.cross(format="csv", file="population_z006.csv", genotypes=c("0","1","2"), crosstype = "riself")## --Read the following data:
## 185 individuals
## 1106 markers
## 29 phenotypes
## --Cross type: riselfObserve que existem alguns avisos sobre:
1: A exclusão das heterozigotes remanescentes em uma população RIL (code==2 é a mesma do genótipo=1); 2: O fato de que o mapa ainda não é estimado, então o pacote interpreta cada marcador de 10 cM de distância (o que é claramente errado e vamos corrigi-lo).qtl
population_Z006 <- est.rf(cross = population_Z006)
plotRF(population_Z006, col.scheme = "redblue")
A partir da função, notamos que 1106 marcadores ainda não foram
atribuídos a grupos de vinculação. Na prática, recomenda-se verificar a
falta de dados de marcadores e distorção de segregação antes do
agrupamento, mas vamos ignorá-los por uma questão de tempo
aqui.plotRF()
Agrupamento de marcadores
Uma vez que tenhamos as estimativas de fração de recombinação de pairwise, podemos tentar ver quais marcadores estão no mesmo grupo de ligação. Para isso, precisamos fornecer a fração máxima de recombinação (argumento) e pontuação mínima de LOD (argumento). Esses valores são fornecidos à função e são usados para ver se dois marcadores estão ligados ou não, evitando falsos positivos. Vamos mostrar de onde esses valores vêm, mas você pode usá-los diretamente em sua análise.max.rfmin.lodformLinkageGroups()
Para , podemos usar algo em torno de 0,38, que é a fração de recombinação máxima de valor de 0,50 quando convertido via função de mapa Kosambi:max.rf
Uma fração de recombinação tão grande quanto 0,50 significa que dois marcadores estão segregando independentemente (ou seja, esses dois marcadores não estão ligados). Veja abaixo:
max.rf <- 0.38
kosambi <- function(r) (1/4)*log((1+(2*r))/(1-(2*r)))
kosambi(r = max.rf)## [1] 0.4981075Pois, podemos executar a correção bonferroni no número de testes que temos que realizar para avaliar a vinculação do marcador. O número de testes é o número de pares de marcadores que temos em nossos dados. Como primeiro palpite, temos:min.lod
(M <- totmar(population_Z006))## [1] 1106(num.pair <- choose(M, 2))## [1] 611065(min.lrt <- qchisq(1-(0.05/num.pair), 1))## [1] 28.7624(min.lod <- 0.2172 * min.lrt)## [1] 6.247193Agora, é hora de ver quantos grupos de ligação temos. Primeiro, executamos a função apenas para ver como os marcadores são distribuídos ao longo dos grupos de linkagem formados:formLinkageGroups()reorgMarkers = FALSE
lg <- formLinkageGroups(population_Z006, max.rf=max.rf, min.lod=min.lod, reorgMarkers=FALSE)
table(lg[,2])##
## 1 2 3 4 5 6 7 8 9 10
## 175 139 130 127 111 106 85 78 78 77population_Z006 <- formLinkageGroups(population_Z006, max.rf=0.38, min.lod=6.25, reorgMarkers=TRUE)
plotRF(population_Z006, col.scheme = "redblue")
O mapa de calor mostra marcadores agrupados, mas ainda não ordenados dentro de cada grupo de ligação.
Ordenação de marcadores
Vamos mostrar duas maneiras de ordenar marcadores. A primeira maneira usa a função por R/qtl e geralmente precisa de alguma curadoria manual. A segunda maneira usa o algoritmo MDS e é mais rápido e geralmente mais preciso. Você pode escolher qual método deseja usar e pular para sua seção específica, o que significa que você não precisa executar para os dois lados.orderMarkers()
Usando orderMarkers a função por R/qtl
R/qtl tem uma função que executa o algoritmo Branch-and-Bound como uma possível solução para o Problema do Vendedor de Viagens (TSP) que é o marcador de pedidos. Geralmente fornece uma boa solução. O problema é que Branch-and-Bound é muito sensível à escolha do marcador que é feita para iniciar o algoritmo. Portanto, executamos pelo menos algumas vezes para que algum efeito das primeiras escolhas possa ser avaliado.
Nós salvamos o objeto sob dois novos objetos chamados e, assim, podemos atualizar com os resultados de duas corridas do algoritmo Branch-and-Bound. Além disso, inicializamos dois objetos que armazenarão a probabilidade de registro do pedido para cada grupo de linkage de ambas as corridas:population_Z006.bb1, population_Z006.bb2
population_Z006.bb1 <- population_Z006
population_Z006.bb2 <- population_Z006
loglik.bb1 <- loglik.bb2 <- c()c <- 1
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 2
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 3
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 4
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 5
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 6
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 7
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 8
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 9
plotRF(population_Z006, chr=c, col.scheme = "redblue")
c <- 10
plotRF(population_Z006, chr=c, col.scheme = "redblue")
memory.limit(9999999999)## [1] 1e+10Grupo de ligação 1
O objeto armazena o número do cromossomo em avaliação.c
c <- 1
plotRF(population_Z006, chr=c, col.scheme = "redblue")
O argumento vamos dar uma olhada mais de perto no mapa de calor de um
cromossomo específico, cujos marcadores claramente não são
ordenados.chr
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA00276.18 PZB01227.6 PZA02044.1 PZA00307.14 PZA00991.2
## 0.000000e+00 5.000001e-06 1.000000e-05 1.500000e-05 6.293577e+00
## PZA00235.9 PZA02359.10 PZA00623.3 PHM9807.9 PZA01238.1.2
## 7.461849e+00 9.052711e+00 9.052716e+00 1.014222e+01 1.049885e+01
## PZA01068.1 PZA00343.31 PHM1275.22 PZA00856.2 PZA00243.25
## 1.049885e+01 1.092123e+01 1.197655e+01 1.197724e+01 1.197724e+01
## PZA01239.2 PHM7616.35 PZA01807.1 PZA00432.4 PZA03613.1
## 1.536560e+01 1.536561e+01 1.536561e+01 1.536562e+01 4.009893e+02
## PZA01271.1 PZA02129.1 PZA02032.1 PHM2244.142 PZA02372.1
## 4.009893e+02 4.034567e+02 4.043264e+02 4.097052e+02 4.121396e+02
## PHM6238.36 PZA00175.2 PZA00528.1 PZA00447.8 PZA00181.2
## 4.134812e+02 4.152862e+02 4.152862e+02 4.152862e+02 4.152862e+02
## PZA02284.1 PZA00731.7 PZA00566.5 PZA00887.1 PZA00106.10
## 4.199949e+02 4.199949e+02 4.220410e+02 4.229607e+02 4.229607e+02
## PZA03521.1 PZA03551.1 PZA01497.1 csu1171.2 PZA02393.2
## 4.229607e+02 4.250480e+02 4.311016e+02 4.311016e+02 4.347221e+02
## PZB00648.5 PZA01652.1 PZA02094.9 PZB00718.5 PZA01030.1
## 4.347221e+02 4.347221e+02 4.347221e+02 4.362755e+02 4.381024e+02
## PZA00425.11 PHM13619.5 PHM3226.15 PHM4531.46 PZB01957.1
## 4.455412e+02 4.455412e+02 4.487030e+02 4.487030e+02 4.487030e+02
## PZA02487.1 PZA01455.1 PZB02058.1 PZA01348.1 PZA02490.1
## 4.487030e+02 4.532509e+02 4.532509e+02 4.532509e+02 4.532509e+02
## PZB01662.1 PZA02686.1 PZA02271.1 PZA02195.1 PHM3726.129
## 4.532509e+02 4.532509e+02 4.575621e+02 4.578865e+02 4.608627e+02
## PZA00240.6 PZA02376.1 PZA00962.1 PZA03742.1 PZA03243.2
## 4.608627e+02 4.612116e+02 4.612116e+02 4.625847e+02 4.625847e+02
## PZA00081.18 umc13.1 PHM4913.18 PZA03183.5 PZB00872.3
## 4.635346e+02 4.641480e+02 4.644696e+02 4.644696e+02 4.644696e+02
## PZA02292.1 PZA03168.5 PZA02737.1 PZA02550.1 PZA02114.1
## 4.662643e+02 4.662643e+02 4.700346e+02 4.704035e+02 4.707422e+02
## PZB01062.3 PZA03561.1 PZA01315.1 PZA01476.1 PZA00294.22
## 4.707422e+02 4.707450e+02 4.717013e+02 4.720165e+02 4.730182e+02
## PZA03189.4 PZA01267.3 PHM5098.25 PZA00752.1 PZA01135.1
## 4.733646e+02 4.796684e+02 4.796684e+02 4.817195e+02 4.832863e+02
## PZA03240.1.2 PZA00944.1.2 PZA03465.1 PZB01235.4 PZA02577.1
## 4.862019e+02 4.865480e+02 4.865480e+02 4.887153e+02 4.917986e+02
## PZA03200.2 csu1138.3.4 PZA02070.1 PHM9418.11 PZA02763.1
## 4.917986e+02 4.924350e+02 4.924350e+02 4.930801e+02 4.930801e+02
## PZA01254.2 PZA00939.1 PZA02750.3 PZA03037.2 PZA03305.7.1
## 4.930801e+02 4.930801e+02 4.930801e+02 5.675905e+02 5.675905e+02
## PZA00894.7 PZB01403.1 PHM18705.23 PZA02087.2 PZA01246.1
## 5.678855e+02 5.690750e+02 5.694148e+02 5.711603e+02 5.711603e+02
## PZA00245.20 PZA00978.1 PZA03020.8 PZA02957.5 PZA03188.3
## 5.711603e+02 5.714463e+02 5.717231e+02 5.720144e+02 5.744005e+02
## PZA00610.16 PZA02204.1 PZB00114.1 PZA01978.23 PZA02520.1
## 5.752629e+02 5.778977e+02 5.789900e+02 5.789900e+02 5.807452e+02
## PZA00030.11 PZA02698.3 PZB00895.1 PZA02278.1 PZA01921.20.19
## 5.852413e+02 5.852414e+02 5.875087e+02 5.875087e+02 5.948660e+02
## PZA00339.4 PZA03457.1 PZA02985.5 PHM5526.25 PZB00008.1
## 5.948660e+02 5.960090e+02 5.960090e+02 5.960090e+02 5.960090e+02
## PZB00063.1 PHM14475.7 PZA01588.1 glb1.2 kip1.3
## 5.960090e+02 6.030956e+02 6.030956e+02 6.030956e+02 6.041978e+02
## PHM3034.3 PZA02269.3.4 PZA03404.1 PHM16605.19 PZA03064.6
## 6.041978e+02 6.070207e+02 6.070207e+02 6.092271e+02 6.099434e+02
## PZA00381.4 PZA03301.2 PZA03001.15 PHM4926.16 PZA00664.3
## 6.145909e+02 6.145909e+02 6.145909e+02 6.145909e+02 6.218464e+02
## PZA02186.1 PZB01647.1 PZA00658.21 umc128.2 PZA02823.1
## 6.218464e+02 6.218464e+02 6.229414e+02 6.229414e+02 6.229414e+02
## PHM2478.22 PHM4942.12 PZA02117.1 PHM5484.22 PHM15871.11
## 6.229414e+02 6.229414e+02 6.235334e+02 6.250205e+02 6.250205e+02
## PZA03741.1 PHM6043.19 PZA01039.1 PHM12706.14 PZA03265.3
## 6.283075e+02 6.289283e+02 6.318643e+02 6.318643e+02 6.318643e+02
## PZA02014.3 PHM5480.17 PZA03193.2 PZA03194.1 PZA01019.1
## 6.318643e+02 6.324572e+02 6.324572e+02 6.324572e+02 6.399156e+02
## PZA01963.15 PZA00131.15 PZA01391.1 PZA01216.1 PZA03074.27
## 6.399157e+02 6.399157e+02 6.399157e+02 6.399157e+02 6.420129e+02
## PZA00619.3 PZA02467.10 PZA03531.1 PZA00068.1 PHM1968.22
## 6.462185e+02 6.462185e+02 6.582310e+02 6.582310e+02 6.588563e+02
## PZA00455.14.16 PZA02191.1 PZA02135.2 PZA02741.1 an1.5
## 6.626041e+02 6.626041e+02 6.660536e+02 6.660536e+02 6.660536e+02(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -2839.988population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA02044.1 PZA00307.14 PZA00991.2 PZA00276.18 PZA00235.9
## 0.000000 6.288824 6.288829 6.288834 7.457576
## PZA00623.3 PHM9807.9 PZA02359.10 PZA01238.1.2 PZA01068.1
## 9.048323 10.137840 10.137845 10.494573 10.494578
## PZA00343.31 PZA00856.2 PHM1275.22 PZA00243.25 PZA01239.2
## 10.916949 11.972992 11.972997 11.973002 15.361358
## PZA00432.4 PHM7616.35 PZA01807.1 PZA03613.1 PZA01271.1
## 15.361363 15.361368 15.361373 400.985085 400.985090
## PZA02129.1 PZA02032.1 PHM2244.142 PZA02372.1 PHM6238.36
## 403.452499 404.322147 409.700986 412.135340 413.476917
## PZA00528.1 PZA00181.2 PZA00175.2 PZA02284.1 PZA00447.8
## 415.281921 415.281926 415.281931 419.990616 419.990621
## PZA00731.7 PZA00566.5 PZA00887.1 PZA00106.10 PZA03521.1
## 419.990626 422.036740 422.956400 422.956405 422.956410
## PZA03551.1 PZA01497.1 csu1171.2 PZA01652.1 PZA02094.9
## 425.043729 431.097527 431.097532 431.097537 431.097542
## PZB00648.5 PZA02393.2 PZB00718.5 PZA01030.1 PZA02487.1
## 434.718503 434.718508 436.271561 438.098506 445.538502
## PHM4531.46 PHM13619.5 PZA00425.11 PZB02058.1 PHM3226.15
## 445.538507 445.538512 445.538517 448.697781 448.697786
## PZB01957.1 PZA02686.1 PZA01348.1 PZB01662.1 PZA01455.1
## 448.697791 453.233400 453.233405 453.233410 453.233415
## PZA02490.1 PZA02271.1 PZA02195.1 PHM3726.129 PZA00240.6
## 453.233420 457.497182 457.818630 460.724749 460.724754
## PZA00962.1 PZA02376.1 PZA00081.18 PZA03742.1 PZA03243.2
## 461.066486 461.066491 463.358503 464.163812 464.163817
## umc13.1 PHM4913.18 PZA03183.5 PZB00872.3 PZA02292.1
## 465.719033 466.038898 466.038903 466.038908 467.839575
## PZA03168.5 PZA02737.1 PZA02550.1 PZA02114.1 PZA03561.1
## 467.839580 471.507929 471.868818 472.200044 472.200049
## PZB01062.3 PZA01315.1 PZA01476.1 PZA03189.4 PZA00294.22
## 472.200054 473.086287 473.378085 474.291576 474.307165
## PZA03064.6 PHM16605.19 PZA02269.3.4 PZA03404.1 PHM3034.3
## 517.872586 518.466266 520.333343 520.333348 522.857043
## kip1.3 PZA01588.1 glb1.2 PHM14475.7 PZA00339.4
## 522.857048 523.922441 523.922446 523.922451 529.407623
## PZA01921.20.19 PZA03457.1 PZA02985.5 PZB00008.1 PHM5526.25
## 529.407628 530.555552 530.555557 530.555562 530.555567
## PZB00895.1 PZB00063.1 PZA02278.1 PZA02698.3 PZA00030.11
## 537.078788 537.078793 537.078798 539.364352 539.364357
## PZA02520.1 PZB00114.1 PZA01978.23 PZA02204.1 PZA00610.16
## 543.860397 545.607606 545.607611 546.692017 549.310856
## PZA03188.3 PZA02957.5 PZA03020.8 PHM18705.23 PZA00245.20
## 550.168619 552.535981 552.535986 554.424959 554.424964
## PZA03305.7.1 PZB01227.6 PZA03037.2 PZA00894.7 PZB01403.1
## 556.645712 556.645717 556.929700 557.213387 558.367382
## PZA01246.1 PZA02087.2 PZA00978.1 PHM4926.16 PZB01647.1
## 560.422122 560.422127 560.701663 599.531169 599.531174
## PZA00381.4 PZA03301.2 PZA03001.15 PZA02186.1 umc128.2
## 599.531179 599.531184 605.264311 605.264316 606.346351
## PHM2478.22 PZA02823.1 PHM4942.12 PZA00664.3 PZA00658.21
## 606.346356 606.346361 606.346366 606.346371 606.346376
## PZA02117.1 PHM5484.22 PHM15871.11 PZA03741.1 PZA03265.3
## 606.934838 608.413868 608.413873 611.705493 612.327397
## PHM6043.19 PZA02014.3 PZA01039.1 PZA03193.2 PHM12706.14
## 612.327402 615.267161 615.267166 615.859923 615.859928
## PZA00131.15 PHM5480.17 PZA01391.1 PZA03194.1 PZA01963.15
## 623.363858 623.363863 623.363868 623.363873 623.363878
## PZA01019.1 PZA01216.1 PZA03074.27 PZA00619.3 PZA02467.10
## 623.363883 623.363888 625.467525 629.676025 629.676030
## PZA03531.1 PZA00068.1 PHM1968.22 PZA00455.14.16 PZA02191.1
## 641.787032 641.787037 642.421202 646.373886 646.373891
## an1.5 PZA02135.2 PZA03200.2 PZA02741.1 PZA02577.1
## 650.249523 655.662626 655.662631 655.662636 656.008618
## csu1138.3.4 PZA02070.1 PZA01254.2 PHM9418.11 PZA02763.1
## 656.012638 656.705913 657.457929 657.457934 657.457939
## PZA02750.3 PZA00939.1 PZB01235.4 PZA00944.1.2 PZA03465.1
## 657.457944 657.457949 659.649212 661.727835 661.727840
## PZA03240.1.2 PZA01135.1 PZA00752.1 PHM5098.25 PZA01267.3
## 662.060832 664.841344 666.328429 668.149776 668.149781(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -2934.306save.image("population_Z006.RData")Grupo de ligação 2
c <- 2
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA01887.1 PZA02367.1 PZA00191.5 PZA00818.1 PZA01983.1
## 0.0000000 0.2779921 0.5660755 0.5660805 0.8236731
## PZA01438.1 PHM5359.10 PZA02316.22 PZA01570.1 PHM13122.43
## 9.1412540 9.1422131 9.1422444 11.8491798 16.6241608
## PZA02653.12 PZA02462.1 PZA02753.1 PZB00054.3 PHM3137.17
## 24.1729660 27.8546096 28.2886521 28.2886571 31.5549217
## PZA01371.1 PZB00094.1 PZA02029.21 PZA01925.1 PZA00865.1
## 34.6890596 34.6890646 34.6890696 34.6890746 35.0522466
## PZB00079.4 PZA01284.6 PZA03092.7 PZA00112.5 PZA00985.1
## 41.7941541 43.4329278 43.4329328 45.3286365 46.4896298
## PZA01327.1 PZA01523.1 PZA03578.1 PZA03226.3 PZA03274.4
## 49.5321777 51.4228663 51.4228713 53.2218509 53.2218559
## PZA00517.7 PZA01427.1 PZA02792.26.25 PZA02113.1 PHM4647.8
## 53.2218609 55.1219620 55.1219670 59.8232999 59.8233049
## PHM565.31 PHM12992.5 PZA00934.2 PZA01530.1 PZA00499.3
## 59.8233099 59.8233149 59.8233199 59.8233249 59.8233299
## PHM1870.20 PZA00801.1 PZB00869.4 PZA01804.1 PHM16854.3
## 67.4766000 67.4766050 67.4766100 67.4766150 67.4766200
## PZA00805.1 PZA00222.7 PZA03451.5 PZA00981.3 PZA00522.12.7
## 67.4766250 67.4766300 67.4766350 67.4766400 67.4766450
## PZA02207.1 PZA01563.1 PZA00996.1 PHM3691.18 PZB01115.3
## 67.4766500 67.4766550 67.4766600 67.4766650 68.5212602
## PZA02676.2 PHM3171.5 PHM4165.14 PZB00232.2 PZA02525.1
## 68.5212652 69.1848733 69.5185385 69.5185435 70.7085083
## PZA03677.1 PZA01050.1 PHM5798.39 PZB01112.1 PZA01349.2
## 70.7085133 70.7085183 70.7085233 70.7085283 70.7085333
## PZA00261.6 PZA01303.1 PZA02862.3 PZA02818.6 PZA01779.1
## 71.6210732 71.6210782 73.2327834 73.4100799 73.5901497
## PZA00273.5 PZA01693.1 PZA03049.24 PZA00643.13 PZA00881.1
## 75.2314812 75.2314862 76.5525257 77.8678294 77.8678344
## PZA02164.16 PZA01365.1 PZB01017.1 PZA00067.10 PZA01608.1
## 77.8678394 80.7937851 81.8624047 81.8624097 83.9485857
## PZA01796.1 PZA00148.3 PZA02981.2 PZA01763.2 ae1.8.7
## 84.5930243 86.3636287 88.8937108 88.8937158 88.8937208
## PZA03536.1 PZA02641.2 PZA01294.2.1 PZA00255.14 PZA00987.1
## 89.5254044 89.5254094 89.5254144 89.5254194 90.9142109
## PZA02040.2 PZA00300.14 PZA01410.1 PZA03717.1 PZA03714.1
## 90.9142159 90.9142209 91.2907777 94.6395126 94.6395176
## PZA02633.4 PHM5296.6 PZA02356.7 PZA02411.3 PZA02751.1
## 95.4326478 95.4326528 95.4326578 95.4326628 95.4326678
## PZA03452.6 PZA03324.1 PZA03317.1 PZA02408.2 PHM532.23
## 95.4326728 95.4326778 95.4326828 95.4326878 95.4326928
## PZA03320.6 PHM1899.157 PZA03172.3 PZA01304.1 PZA02209.2
## 95.4326978 95.4327028 95.4327078 95.4327128 95.4327178
## PZA02426.1 PZA00352.23 PZA01575.1 PZA02383.1 PZA00652.17
## 95.4327228 95.4327278 95.4327328 95.4327378 99.1277731
## PZA03024.16 PZA01142.4 PZA01265.1 PZA02820.17 PZA00395.2
## 99.4709981 99.8151822 101.5064565 101.5064615 101.8144177
## PZA02060.1 PZA02667.1 PZA02513.1 PZB00765.1 PHM3512.186
## 101.8144227 101.8144277 101.8144327 101.8144377 103.0302873
## PZA00963.3 PZA00980.1 PZA03167.5 PZA02068.1 PZA02015.11
## 105.4007468 105.4007518 119.2762911 119.2762961 119.2763011
## PZA01680.3 PZA00545.26 PZA00836.1 PZA01259.1 PZA01140.1
## 119.2763061 119.2763111 119.2763161 119.2763211 134.5669871
## PZA02480.1 PZA01060.1 PZA02390.1 PZA02769.1
## 150.3227178 150.3227228 150.3227278 150.3227328(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1670.498population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA00818.1 PZA01887.1 PZA01983.1 PZA02367.1 PZA00191.5
## 0.0000000 0.2743339 0.2743389 0.2743439 8.6622664
## PZA02316.22 PZA01438.1 PZA01570.1 PHM5359.10 PHM13122.43
## 8.6622714 8.6622764 11.3762952 11.3763002 16.1537513
## PZA02653.12 PZA02462.1 PZB00054.3 PHM3137.17 PZA02753.1
## 23.7017531 27.3831429 27.8168702 31.0831248 31.0831298
## PZB00094.1 PZA01371.1 PZA02029.21 PZA01925.1 PZA00865.1
## 34.2176073 34.2176123 34.2176173 34.2176223 34.5807953
## PZB00079.4 PZA01284.6 PZA03092.7 PZA00112.5 PZA00985.1
## 41.3227180 42.9614931 42.9614981 44.8572015 46.0181948
## PZA03578.1 PZA01327.1 PZA01523.1 PZA03274.4 PZA00517.7
## 49.0609315 49.0609365 50.9516250 52.7504102 52.7504152
## PZA03226.3 PZA02792.26.25 PZA01563.1 PZA00981.3 PHM565.31
## 52.7504202 54.6501375 59.3519594 59.3519644 59.3519694
## PZA00934.2 PZA00499.3 PHM3691.18 PZA02113.1 PZA01427.1
## 59.3519744 59.3519794 59.3519844 59.3519894 59.3519944
## PZA00801.1 PZB00869.4 PZA00805.1 PHM12992.5 PZA01530.1
## 67.0052398 67.0052448 67.0052498 67.0052548 67.0052598
## PZA02207.1 PHM4647.8 PZA03451.5 PZA00522.12.7 PHM16854.3
## 67.0052648 67.0052698 67.0052748 67.0052798 67.0052848
## PZA00996.1 PZA01804.1 PZA00222.7 PZA02676.2 PHM1870.20
## 67.0052898 67.0052948 67.0052998 68.0497129 68.0497179
## PHM3171.5 PZB01115.3 PZB00232.2 PZA01349.2 PZA02525.1
## 68.7134291 68.7134341 69.0470459 70.2371498 70.2371548
## PZB01112.1 PZA03677.1 PZA01050.1 PHM4165.14 PZA01303.1
## 70.2371598 70.2371648 70.2371698 70.2371748 71.1493698
## PHM5798.39 PZA02862.3 PZA00261.6 PZA01779.1 PZA02818.6
## 71.1493748 72.7610092 72.7610142 73.1200787 73.1200837
## PZA00273.5 PZA01693.1 PZA03049.24 PZA00643.13 PZA02164.16
## 74.7614041 74.7614091 76.0824257 77.3977220 77.3977270
## PZA00881.1 PZA01365.1 PZA00067.10 PZB01017.1 PZA01608.1
## 77.3977320 80.3236686 81.3920803 83.4768105 83.4781246
## PZA01796.1 PZA00148.3 PZA02981.2 PZA01763.2 ae1.8.7
## 84.1227705 85.8933716 88.4234488 88.4234538 88.4234588
## PZA01294.2.1 PZA03536.1 PZA02641.2 PZA00255.14 PZA02040.2
## 89.0551423 89.0551473 89.0551523 89.0551573 90.4439470
## PZA00300.14 PZA00987.1 PZA01410.1 PZA03714.1 PZA03717.1
## 90.4439520 90.4439570 90.8205082 94.1692381 94.1692431
## PHM5296.6 PZA00352.23 PZA03317.1 PZA02426.1 PZA02383.1
## 94.9623718 94.9623768 94.9623818 94.9623868 94.9623918
## PZA02356.7 PZA03172.3 PZA03320.6 PZA01304.1 PZA02751.1
## 94.9623968 94.9624018 94.9624068 94.9624118 94.9624168
## PZA02411.3 PZA03324.1 PZA02408.2 PZA01575.1 PHM532.23
## 94.9624218 94.9624268 94.9624318 94.9624368 94.9624418
## PHM1899.157 PZA03452.6 PZA02209.2 PZA02633.4 PZA00652.17
## 94.9624468 94.9624518 94.9624568 94.9624618 98.6574903
## PZA03024.16 PZA01142.4 PZA01265.1 PZA02820.17 PZA02060.1
## 99.0007148 99.3448985 101.0361668 101.0361718 101.3441271
## PZB00765.1 PZA02667.1 PZA02513.1 PZA00395.2 PHM3512.186
## 101.3441321 101.3441371 101.3441421 101.3448412 102.5599524
## PZA00963.3 PZA00545.26 PZA00836.1 PZA01259.1 PZA01680.3
## 104.9294220 118.8087989 118.8088039 118.8088089 118.8088139
## PZA02068.1 PZA00980.1 PZA02015.11 PZA03167.5 PZA01140.1
## 118.8088189 118.8088239 134.0949728 134.0949778 134.0949828
## PZA01060.1 PZA02390.1 PZA02480.1 PZA02769.1
## 149.8522588 149.8522638 149.8522688 149.8522738(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -1667.511save.image("population_Z006.RData")Grupo de ligação 3
c <- 3
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA00088.3 PZA02423.1 PHM2423.33 PZA02182.1 PHM3852.23
## 0.000000e+00 5.000001e-06 2.390701e+00 1.038442e+01 1.138521e+01
## PZA01688.3 PZA01360.3 PZA00316.10 PZA00402.1 PZA00219.7
## 1.138522e+01 1.138522e+01 1.235097e+01 1.887097e+01 2.287366e+01
## PHM2672.19 PZA03391.1 PZA02668.2 PZA02514.1 PZA02824.4
## 2.287367e+01 2.287367e+01 2.471800e+01 2.593521e+01 2.593522e+01
## PZA01154.1 PZA01233.1 sh2.21 PZA00750.1 PHM3342.31
## 2.593522e+01 2.593523e+01 3.050695e+01 3.050695e+01 3.050696e+01
## PZA02665.2 PZA03146.4 PZA02616.1 PZB01457.1 PZA02516.1
## 3.050696e+01 3.114506e+01 3.808368e+01 3.808368e+01 4.008746e+01
## PZA00538.18.15 PZA02733.1 PZA03154.4 PZA00892.5 PZA01501.1
## 4.388314e+01 4.994504e+01 4.994504e+01 5.242760e+01 5.430068e+01
## PZA02122.9 PZA00308.24 PZA01035.1 PZB01109.1 PZA01457.1
## 5.463352e+01 5.733122e+01 5.733122e+01 5.733123e+01 5.733123e+01
## PZA03255.1 PZA01228.2 PHM13673.53 PHM824.17 PZA00494.2
## 5.934354e+01 6.062248e+01 6.062248e+01 6.062249e+01 6.062249e+01
## PZA03647.1 PZA03744.1 PZA03743.1 PZA03191.1.4 zb27.1
## 6.062250e+01 6.062250e+01 6.062251e+01 6.270783e+01 6.270783e+01
## PZA03733.1 PZA03735.1 PHM1675.29 PZA01962.12 PZA02654.3
## 6.270784e+01 6.270784e+01 7.019127e+01 7.458216e+01 7.458216e+01
## PHM17210.5 PZA02212.1 PZA01726.1 PZA00783.1 PZA03032.19
## 7.458217e+01 8.128863e+01 8.187174e+01 8.379991e+01 8.410841e+01
## PZD00027.2 PHM1959.26 PZA02402.1 PZA03073.28.26 PZA00186.4
## 8.514585e+01 8.514586e+01 8.514586e+01 8.629429e+01 8.744412e+01
## PHM2885.31 PZA01396.1 PHM4621.57 PHM9914.11 PZA00667.2
## 8.744412e+01 8.858949e+01 8.858949e+01 8.981643e+01 8.981644e+01
## PZB02179.1 PZA00828.2 PZA00948.1 PZA00827.1 PZA01934.6
## 9.329339e+01 9.329339e+01 9.384030e+01 9.458220e+01 9.532615e+01
## PHM4955.12 PZB02044.1 PHM890.20 PZB02122.1 PZA02474.1
## 9.532615e+01 9.597938e+01 9.597939e+01 9.597939e+01 9.640493e+01
## PZA00920.1 PZD00015.5 PZD00016.4 PZA00363.7 PHM1745.16
## 9.640494e+01 9.897006e+01 9.897006e+01 9.897007e+01 9.897007e+01
## PZB02002.1 PZA02299.16 PZA00413.20.18 PHM15449.10 PZA03198.3
## 9.897008e+01 1.004457e+02 1.011380e+02 1.011380e+02 1.019054e+02
## PZA02619.1 PZA02134.3 PZA02742.1 PHM5502.31 PZA00707.9
## 1.036500e+02 1.045814e+02 1.045814e+02 1.045814e+02 1.045814e+02
## PZA02699.1 PZA02645.2 PZA02296.1 PZA02589.1 PZA00265.6
## 1.045814e+02 1.045814e+02 1.057533e+02 1.057556e+02 1.057556e+02
## PZA00509.1 PZA01447.1 PZA00279.2 PHM15474.5 PZA03119.1
## 1.057556e+02 1.062046e+02 1.062046e+02 1.062046e+02 1.062046e+02
## zb21.1 PZA01114.2 PHM15899.9 PZA00380.10 PZA00348.11
## 1.071155e+02 1.071155e+02 1.071155e+02 1.071155e+02 1.071156e+02
## PZA00297.2 PZA03070.9 PHM13823.7 PZA00581.3 PHM2343.25
## 1.071156e+02 1.071156e+02 1.071156e+02 1.071156e+02 1.074150e+02
## PZA02255.2 PZA03054.5 PZA01473.1 PZA02427.1 PZA00210.1.9
## 1.074150e+02 1.074150e+02 1.074150e+02 1.074150e+02 1.074150e+02
## PHM4145.18 PHM4204.69 PZA01765.1 PZA00508.2 PZA02098.2
## 1.113045e+02 1.198512e+02 1.269967e+02 1.269967e+02 1.315468e+02
## PZB01944.1 PZA00749.1 PZA03212.3 PHM12859.7 PZA03527.1
## 1.315468e+02 1.347438e+02 1.350852e+02 1.383729e+02 1.383729e+02
## PZA00100.10 PZA02678.1 PZA02090.1 PZD00038.2 PZA00309.1
## 1.402500e+02 1.405749e+02 1.486642e+02 1.486642e+02 1.578291e+02(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1926.808population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA00088.3 PZA00309.1 PZA02090.1 PZA02678.1 PZD00038.2
## 0.0000 385.6237 394.7879 402.8771 402.8771
## PZA00100.10 PZA03527.1 PHM12859.7 PZA03212.3 PZA00749.1
## 403.2028 405.0799 405.0799 408.3675 408.7090
## PZB01944.1 PZA02098.2 PZA00508.2 PZA01765.1 PHM4204.69
## 411.9063 411.9063 416.4543 416.4543 423.5957
## PHM4145.18 PZA02427.1 PHM2343.25 PHM13823.7 PZA01114.2
## 432.1496 433.9901 435.8303 436.1620 436.1620
## PZA03070.9 PZA00297.2 PZA00380.10 PZA01473.1 PZA02255.2
## 436.1620 436.1620 436.1620 436.4842 436.4842
## PZA00210.1.9 zb21.1 PZA03054.5 PZA00348.11 PHM15899.9
## 436.4842 436.4842 436.4842 436.4842 437.1201
## PZA00279.2 PHM15474.5 PZA01447.1 PZA00581.3 PZA03119.1
## 437.7559 437.7559 437.7560 437.7560 437.7560
## PZA00265.6 PZA00509.1 PZA02589.1 PZA02699.1 PZA02645.2
## 438.1773 438.1773 438.1773 438.7127 439.2519
## PZA02742.1 PZA02296.1 PZA00707.9 PHM5502.31 PZA02134.3
## 439.2519 439.2519 439.2519 439.2519 439.2519
## PZA02619.1 PZA00413.20.18 PZA03198.3 PHM15449.10 PZA02299.16
## 440.1568 442.7276 442.7276 442.7276 443.4237
## PHM1745.16 PZD00015.5 PZD00016.4 PZB02002.1 PZA00363.7
## 444.9102 444.9102 444.9102 444.9102 444.9102
## PZA00920.1 PZA02474.1 PZB02044.1 PHM890.20 PZB02122.1
## 447.4955 447.4955 447.9248 447.9248 447.9248
## PHM4955.12 PZA01934.6 PZA00827.1 PZA00948.1 PZA00828.2
## 448.5771 448.5771 449.3231 450.0670 450.6151
## PZB02179.1 PHM9914.11 PZA00667.2 PZA01396.1 PHM4621.57
## 450.6151 452.2825 453.9539 455.1905 455.7583
## PHM2885.31 PZA00186.4 PHM1959.26 PZA03073.28.26 PZD00027.2
## 456.3248 456.3248 457.4781 457.4781 458.6272
## PZA03032.19 PZA02402.1 PZA00783.1 PZA01726.1 PZA02212.1
## 459.6645 459.6645 459.9731 461.9015 462.4847
## PZA02654.3 PHM17210.5 PZA01962.12 PZA03733.1 PZA03735.1
## 469.1920 469.1920 469.1920 473.5837 473.5837
## PHM1675.29 zb27.1 PZA03191.1.4 PZA03744.1 PZA00494.2
## 473.5837 481.0650 481.0650 483.1496 483.1496
## PZA03647.1 PZA01228.2 PHM824.17 PHM13673.53 PZA03743.1
## 483.1496 483.1496 483.1497 483.1497 483.1497
## PZA03255.1 PZA00308.24 PZA01457.1 PZB01109.1 PZA01035.1
## 484.4278 486.4389 486.4389 486.4389 486.4389
## PZA02122.9 PZA01501.1 PZA00892.5 PZA03154.4 PZA00538.18.15
## 489.1350 489.4676 491.3391 493.8190 499.8774
## PZA02733.1 PZA02516.1 PZB01457.1 PZA02616.1 PHM3342.31
## 499.8774 503.6581 505.6357 505.6358 513.4295
## PZA02665.2 PZA01154.1 sh2.21 PZA03146.4 PZA00750.1
## 513.4295 513.4295 513.4295 513.4295 513.6918
## PZA02824.4 PZA02514.1 PZA01233.1 PZA02668.2 PHM2672.19
## 518.5797 518.5797 518.5797 519.7940 521.6335
## PZA00219.7 PZA03391.1 PZA00402.1 PZA00316.10 PZA01360.3
## 521.6335 521.6335 525.6290 532.1458 533.1112
## PZA01688.3 PHM3852.23 PZA02182.1 PHM2423.33 PZA02423.1
## 533.1112 533.1112 534.1117 542.1033 544.4887(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -2089.495save.image("population_Z006.RData")Grupo de ligação 4
c <- 4
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA00680.3 PHM5817.15 PZA00365.2 PHM1511.14 PZA02133.10 PZA00525.17
## 0.000000e+00 5.000001e-06 1.000000e-05 4.151674e+00 4.151679e+00 4.151684e+00
## PHM13440.13 PZA02681.8 PZA02175.1 PZA00902.1 PZA02264.5 PZB01233.1
## 4.151689e+00 5.043615e+00 5.043620e+00 5.514068e+00 5.984278e+00 8.972924e+00
## PZA01211.1 PZA00613.22 PZA00172.12 PZA02208.1 PZA01935.10 PZA02081.1
## 1.127069e+01 1.456996e+01 1.456996e+01 2.006717e+01 2.266478e+01 2.266479e+01
## PZA03747.1 PZA03699.1 PZB00901.3.4 PZA02272.3 PHM5822.15 zfl2.9
## 2.466797e+01 2.466797e+01 3.135588e+01 3.212364e+01 3.368099e+01 4.045350e+01
## PZA01753.1 PZA02417.2 PZA02337.4 PZA03559.1 PZA00108.4 PZA02774.1
## 4.433781e+01 4.477255e+01 4.477255e+01 4.477256e+01 4.524003e+01 7.625137e+01
## PZA02168.1 PZA03629.1 PZA02279.1 PHM3457.6 PZA00635.7 PHM10321.11
## 7.787194e+01 7.787195e+01 8.142744e+01 8.142744e+01 8.142745e+01 8.257966e+01
## PHM13360.13 PZA02549.3 PHM4880.179 PZA01902.1 PZA02626.1 PZA00485.2
## 8.373187e+01 8.373187e+01 8.373188e+01 8.373188e+01 8.698493e+01 8.698493e+01
## PHM3626.3 PZA00029.17 PZA01280.2 PZA03211.6 PZA01537.2 PZA02939.10
## 8.698494e+01 8.698494e+01 8.761734e+01 8.761734e+01 8.823490e+01 8.854758e+01
## PZA01232.1 PZA01321.1 PZA02465.1 PZA00637.6 PZA00515.10 PZA02371.6
## 8.854759e+01 8.991462e+01 8.991463e+01 9.092627e+01 9.092627e+01 9.092628e+01
## PZA03659.1 vdac1a.1 PZA00224.4 PZA03692.1 PZA00495.5 PZA03644.1
## 9.313204e+01 9.313205e+01 9.313205e+01 9.313206e+01 9.313206e+01 9.313207e+01
## PZA01638.1 PZA03184.2 PZA00755.2 PZA03529.1 PZA01735.1 PHM3055.9
## 9.313207e+01 9.774366e+01 1.054470e+02 1.054471e+02 1.054471e+02 1.110744e+02
## PZA00803.3 PZA02017.1 PZA02890.4 PZA00824.2 PHM7953.11 PHM3668.12
## 1.110744e+02 1.110744e+02 1.110744e+02 1.110744e+02 1.110745e+02 1.110745e+02
## PHM16125.47 PZA02731.1 PZA03165.1 PZB01103.2 PZA01885.2 PZA02077.1
## 1.113695e+02 1.113695e+02 1.124189e+02 1.134731e+02 1.145225e+02 1.145225e+02
## PZA00390.7 PZA02329.2 PZA02964.7 PHM14412.4 PZB00772.7 PZA03602.1
## 1.145225e+02 1.145225e+02 1.145225e+02 1.145225e+02 1.193608e+02 1.193614e+02
## PZA02456.1 PZA00804.1 PZA02680.1 PZA02471.5 PZA02418.2 PZA02453.1
## 1.204904e+02 1.216559e+02 1.225299e+02 1.233878e+02 1.233878e+02 1.321860e+02
## PZA02012.7 PZA00527.10 PZA01991.3 PZA00163.4 PZA02564.2 PZB01013.1
## 1.321860e+02 1.321860e+02 1.351605e+02 1.351605e+02 1.354501e+02 1.363782e+02
## PZA02266.3 PZA01895.1 PZA01352.5 PZA02727.1 PHM3094.23 PZA02170.1
## 1.392109e+02 1.398833e+02 1.412780e+02 1.416253e+02 1.544968e+02 1.544968e+02
## PZD00022.5 PZA03577.1 PZA03321.4 PZA02450.1 PZA01820.1 PZB00183.4
## 1.580221e+02 1.590308e+02 1.603725e+02 2.416310e+02 2.416310e+02 2.432605e+02
## PZA02378.7 PZA01993.7 PHM10404.8 PZA02496.1 PZA02058.1 PHM4586.12
## 2.459741e+02 2.459741e+02 2.459741e+02 2.459741e+02 2.470248e+02 2.478473e+02
## PZA01336.1 PZA01374.1 PZA02080.1 PHM1962.33 PZA01755.1 PZA03568.1
## 2.478473e+02 2.492234e+02 2.507083e+02 2.507083e+02 2.507083e+02 2.507083e+02
## PZA03142.5 PZA01879.1 PZA00590.1 PHM6111.5 PZA03634.1 PZA03228.4
## 2.537669e+02 2.555513e+02 2.558964e+02 2.565894e+02 2.572884e+02 2.582429e+02
## PZA00497.4
## 2.591320e+02(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -2125.426population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA00680.3 PHM5817.15 PZA00365.2 PZA00525.17 PHM1511.14 PZA02133.10
## 0.000000e+00 5.000001e-06 1.000000e-05 4.151660e+00 4.151665e+00 4.151670e+00
## PHM13440.13 PZA02681.8 PZA02175.1 PZA00902.1 PZA02264.5 PZB01233.1
## 4.151675e+00 5.043598e+00 5.043603e+00 5.514049e+00 5.984258e+00 8.972889e+00
## PZA01211.1 PZA00172.12 PZA00613.22 PZA02208.1 PZA01935.10 PZA02081.1
## 1.127071e+01 1.457009e+01 1.457009e+01 2.006624e+01 2.266346e+01 2.266347e+01
## PZA03747.1 PZA03699.1 PZB00901.3.4 PZA02272.3 PHM5822.15 zfl2.9
## 2.466640e+01 2.466641e+01 3.134850e+01 3.211542e+01 3.367123e+01 4.039856e+01
## PZA03559.1 PZA02337.4 PZA02417.2 PZA01753.1 PZA00108.4 PZA02774.1
## 4.500433e+01 4.500433e+01 4.500434e+01 4.557281e+01 4.557281e+01 7.657656e+01
## PZA02168.1 PZA03629.1 PZA00635.7 PZA02279.1 PHM4880.179 PZA01902.1
## 7.819744e+01 7.819745e+01 8.175087e+01 8.175088e+01 8.411323e+01 8.411323e+01
## PHM13360.13 PHM10321.11 PZA02549.3 PHM3626.3 PHM3457.6 PZA00029.17
## 8.411324e+01 8.411324e+01 8.411325e+01 8.411325e+01 8.411326e+01 8.736412e+01
## PZA03211.6 PZA00485.2 PZA02626.1 PZA01280.2 PZA01537.2 PZA01232.1
## 8.736412e+01 8.736413e+01 8.736413e+01 8.799661e+01 8.861409e+01 8.892677e+01
## PZA02939.10 PZA01321.1 PZA02465.1 PZA02371.6 PZA03659.1 PZA00495.5
## 8.892677e+01 9.029377e+01 9.029377e+01 9.130516e+01 9.351090e+01 9.351090e+01
## vdac1a.1 PZA01638.1 PZA03692.1 PZA03644.1 PZA00224.4 PZA00637.6
## 9.351091e+01 9.351091e+01 9.351092e+01 9.351092e+01 9.351093e+01 9.351093e+01
## PZA00515.10 PZA03184.2 PZA03529.1 PZA01735.1 PZA00755.2 PZA02017.1
## 9.351094e+01 9.812187e+01 1.058251e+02 1.058251e+02 1.058251e+02 1.114475e+02
## PZA02890.4 PHM7953.11 PZA00803.3 PZA00824.2 PHM3668.12 PHM3055.9
## 1.114475e+02 1.114475e+02 1.114475e+02 1.114475e+02 1.114475e+02 1.114475e+02
## PHM14412.4 PZB01103.2 PZA02731.1 PHM16125.47 PZA03165.1 PZB00772.7
## 1.117424e+02 1.117424e+02 1.117424e+02 1.117424e+02 1.117424e+02 1.150440e+02
## PZA00390.7 PZA02964.7 PZA01885.2 PZA02329.2 PZA02077.1 PZA03602.1
## 1.150440e+02 1.150441e+02 1.150441e+02 1.150441e+02 1.150441e+02 1.198783e+02
## PZA02456.1 PZA00804.1 PZA02680.1 PZA00527.10 PZA02418.2 PZA02471.5
## 1.210067e+02 1.221720e+02 1.230458e+02 1.239036e+02 1.239036e+02 1.239036e+02
## PZA02012.7 PZA02453.1 PZA00163.4 PZA01991.3 PZA02564.2 PZB01013.1
## 1.327015e+02 1.356742e+02 1.356751e+02 1.358195e+02 1.359640e+02 1.368922e+02
## PZA02266.3 PZA01895.1 PZA01352.5 PZA02727.1 PZA02170.1 PZD00022.5
## 1.397249e+02 1.403974e+02 1.417920e+02 1.421394e+02 1.550059e+02 1.585279e+02
## PZA03577.1 PZA03321.4 PHM3094.23 PZA01820.1 PZB00183.4 PZA02450.1
## 1.595365e+02 1.608810e+02 1.608811e+02 2.421707e+02 2.438002e+02 2.438002e+02
## PHM10404.8 PZA01993.7 PZA02496.1 PZA02378.7 PZA02058.1 PZA01336.1
## 2.465140e+02 2.465140e+02 2.465140e+02 2.465140e+02 2.475647e+02 2.483872e+02
## PHM4586.12 PZA01374.1 PZA01755.1 PZA03568.1 PHM1962.33 PZA03142.5
## 2.483872e+02 2.497633e+02 2.512479e+02 2.512479e+02 2.543057e+02 2.543057e+02
## PZA02080.1 PZA01879.1 PZA00590.1 PZA03634.1 PHM6111.5 PZA03228.4
## 2.543057e+02 2.560890e+02 2.564336e+02 2.578478e+02 2.578479e+02 2.588022e+02
## PZA00497.4
## 2.596913e+02(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -2119.866save.image("population_Z006.RData")Grupo de ligação 5
c <- 5
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA03227.1 PZA02509.15 PHM1184.26 PHM2438.28 PHM3301.28
## 0.000000e+00 5.000001e-06 1.000000e-05 4.279240e+00 1.261388e+01
## PZA00436.7 PZA00975.1 PZA00683.4 PZA02358.1 PZA02138.1
## 1.459420e+01 1.940289e+01 1.940290e+01 3.218738e+01 3.218739e+01
## PZA01122.1 PZA02385.6 PHM8527.2 PZA00139.4 PZA01422.3
## 3.218739e+01 3.218878e+01 3.311875e+01 3.787133e+01 3.787134e+01
## PZA03048.18 PZA02002.1 PZA02457.1 PZA02705.1 PHM15427.11
## 3.935653e+01 3.935877e+01 4.052736e+01 4.407979e+01 4.407979e+01
## PZA01713.4 PHM13623.14 PHM5572.19 PZA03247.1 PZA01106.3
## 4.574822e+01 4.574822e+01 4.574823e+01 4.574823e+01 4.574824e+01
## PZA00541.1 PZA03385.1 PZA01751.2 PZA00445.22 PZA00726.8.10
## 4.574824e+01 4.606718e+01 4.606718e+01 4.673467e+01 4.705006e+01
## PHM1307.11 PZA01759.1 PHM14055.6 bt2.7.4 PZA03587.1
## 4.705006e+01 4.705007e+01 4.705007e+01 4.768538e+01 4.800076e+01
## PZA03254.1 PZA02767.1 PZA00218.1 PZA03270.2 PZA03597.1
## 4.800077e+01 4.831655e+01 4.831656e+01 4.831656e+01 4.831657e+01
## PZA03564.1 PZA03203.2 PZA03231.1 PZA00104.1 PZB00093.7
## 4.897766e+01 4.963876e+01 5.029992e+01 5.029993e+01 5.343381e+01
## PZA00704.1 PZA03409.1 PZA02027.1 fea2.3 PZA03459.1
## 5.482375e+01 5.482376e+01 5.552005e+01 5.552006e+01 5.586509e+01
## PZA02147.1 PZA03152.3 PZA02992.15 PZA02982.7 PZA00057.2
## 5.745793e+01 5.919049e+01 5.948583e+01 5.948583e+01 6.381758e+01
## PZA03116.1 PZA01926.1 PZA00453.2 PHM3155.14 PZA01289.1
## 6.841044e+01 6.841045e+01 7.588404e+01 7.742114e+01 7.742115e+01
## PZA00271.1 PZA01477.3 PZA01658.1 PZA01681.1 PZA03275.4.1
## 8.241740e+01 8.276207e+01 8.276208e+01 8.276208e+01 8.473762e+01
## PZA01187.1 PZD00030.2 PZA01976.9 PZA01954.1 PZA02289.2
## 9.006466e+01 9.115027e+01 9.254022e+01 9.254023e+01 9.254023e+01
## PHM3637.14 PZA02194.1 PZA00941.2 PZA01766.1 PZA00344.10
## 9.254024e+01 9.254024e+01 9.996194e+01 9.996194e+01 1.006301e+02
## PZB01461.1 PZA00332.5 PHM4348.16 PZA00193.2 PZA01790.1
## 1.018889e+02 1.027178e+02 1.039873e+02 1.039873e+02 1.054358e+02
## PZA03205.1 PZA01566.1 PZA01810.2 PZA02779.1 PZA01332.2
## 1.057854e+02 1.061905e+02 1.061905e+02 1.065956e+02 1.065956e+02
## PZA02614.2 PZA02421.1 PZA02479.1 PZB01021.1 PZA03081.1
## 1.065956e+02 1.065956e+02 1.070836e+02 1.070836e+02 1.070836e+02
## PZA00155.1 PZA03155.3 PZA00878.2 PZA01367.2 PZA00636.7
## 1.075056e+02 1.085173e+02 1.090144e+02 1.090144e+02 1.090144e+02
## PZA00521.3 PZA00694.6 PZA02151.3 PZA02585.2 PHM5599.20
## 1.107784e+02 1.114306e+02 1.148172e+02 1.151037e+02 1.151037e+02
## PZA00513.1 PZA00529.4 PHM5665.26 PHM2100.21 PZA03322.5.3
## 1.151037e+02 1.210320e+02 1.212614e+02 1.252045e+02 1.252045e+02
## PHM4125.11 PZA01905.12 PZA03598.1 PZA00682.17.2 PZA02239.12
## 1.274726e+02 1.333736e+02 1.333736e+02 1.396528e+02 1.396528e+02
## PZA00282.19
## 1.402493e+02(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1750.669population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA00282.19 PZA01905.12 PZA02239.12 PZA00682.17.2 PZA03598.1
## 0.0000000 0.5965586 0.5965636 0.5965686 6.8764589
## PHM4125.11 PZA03322.5.3 PHM2100.21 PZA02585.2 PHM5665.26
## 12.7773315 15.0453355 15.0455262 18.9932064 18.9932114
## PHM5599.20 PZA00529.4 PZA02151.3 PZA00513.1 PZA00694.6
## 19.1071996 19.2223760 24.8159445 25.0497728 28.7435708
## PZA00521.3 PZA00636.7 PZA01367.2 PZA00878.2 PZA03155.3
## 29.3940348 31.1529001 31.1529051 31.1529101 31.6503039
## PZA00155.1 PZA02479.1 PZA03081.1 PZB01021.1 PZA01332.2
## 32.6627875 33.0809481 33.0809531 33.0809581 33.5631482
## PZA02779.1 PZA02614.2 PZA01566.1 PZA02421.1 PZA01810.2
## 33.5631532 33.5631582 33.9640380 33.9640430 33.9640480
## PZA01790.1 PZA03205.1 PHM4348.16 PZA00193.2 PZA00332.5
## 34.4538852 34.4538902 36.2025769 36.2025819 37.4737414
## PZB01461.1 PZA00344.10 PZA00941.2 PZA01976.9 PZA02289.2
## 38.3036786 39.5641306 40.2324293 47.6035845 47.6035895
## PZA01954.1 PHM3637.14 PZA01766.1 PZA02194.1 PZD00030.2
## 47.6035945 47.6035995 47.6036045 47.6036095 48.9866264
## PZA01187.1 PZA03275.4.1 PZA01658.1 PZA01681.1 PZA01477.3
## 50.0671536 55.3623696 57.3295184 57.3295234 57.3295284
## PZA00271.1 PHM3155.14 PZA01289.1 PZA03116.1 PZA00453.2
## 57.6728362 62.6440694 62.6440744 64.1753088 64.1753138
## PZA01926.1 PZA00057.2 PZA02147.1 PZA03152.3 PZA02992.15
## 71.5826100 76.1449772 81.4147455 82.9257977 83.2127030
## PZA02982.7 PZA03459.1 PZA02027.1 fea2.3 PZA00704.1
## 83.2127080 86.5848118 86.7420882 86.8988676 87.5343154
## PZA03409.1 PZA03231.1 PZB00093.7 PZA00104.1 PZA03203.2
## 87.5343204 88.7754126 88.7754176 91.8924586 92.5488297
## PZA03564.1 PZA03270.2 PZA03597.1 PZA00218.1 PZA02767.1
## 93.2054502 93.8620696 93.8620746 93.8620796 93.8620846
## PZA03587.1 PZA03254.1 PHM1307.11 bt2.7.4 PHM14055.6
## 94.1772874 94.1772924 94.4921079 94.4921129 95.1260570
## PZA01759.1 PZA00726.8.10 PZA00445.22 PZA01751.2 PZA03385.1
## 95.1260620 95.1260670 95.4408159 96.1069586 96.1069636
## PZA00541.1 PZA01106.3 PZA03247.1 PHM13623.14 PHM5572.19
## 96.4250436 96.4250486 96.4250536 96.4250586 96.4250636
## PZA01713.4 PZA02705.1 PHM15427.11 PZA02457.1 PZA03048.18
## 96.4250686 98.0877737 98.0877787 101.6265287 102.7951557
## PZA02002.1 PZA01422.3 PZA00139.4 PHM8527.2 PZA02385.6
## 102.7951607 104.2799055 104.2799105 109.0323076 109.0351936
## PZA02358.1 PZA02138.1 PZA01122.1 PZA00975.1 PZA00436.7
## 109.9632040 109.9632090 109.9632140 122.7108891 127.4834170
## PZA00683.4 PHM3301.28 PZA03227.1 PZA02509.15 PHM1184.26
## 127.4834220 129.4489495 142.2469858 142.2469908 142.2469958
## PHM2438.28
## 146.4434731(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -1793.54save.image("population_Z006.RData")Grupo de ligação 6
c <- 6
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PHM4711.14 PZA00368.1 PZA00416.7 PZA01601.1 PZA03178.1
## 0.000000e+00 5.000001e-06 1.696468e+00 2.022070e+00 1.060627e+01
## PZA01691.1 PHM9695.8 PZA01951.1 PZA01079.1 PZA02955.3
## 1.126837e+01 1.269359e+01 1.375280e+01 1.760666e+01 1.760666e+01
## PZA01357.2 PZA02528.1 PZA02454.2 PHM6428.11 PHM1978.111
## 1.957484e+01 2.260872e+01 2.292831e+01 2.899991e+01 2.899991e+01
## PHM2350.17 PZA00758.1 PZA00498.5 PZA01186.1 PZA00793.2
## 3.100519e+01 3.100519e+01 3.100520e+01 3.100520e+01 3.299124e+01
## PZA01196.2 PZA01209.1 PZA02203.1 PZA01301.1 PZA01470.1
## 3.299125e+01 3.299125e+01 3.299126e+01 3.451841e+01 3.451841e+01
## PZA00717.15 PZA01960.1 PZA00379.2 PZA01257.1 PZA01363.2
## 3.480853e+01 3.480853e+01 3.480854e+01 3.480854e+01 3.509507e+01
## PHM11114.7 PZA01297.1 PZA02522.1 PZA02683.1 PHM3978.104
## 3.509507e+01 3.731949e+01 3.731950e+01 3.861280e+01 3.990609e+01
## PZA00908.2 PZA03135.1 PZA02019.1 PZA03579.1 PHM4134.8
## 3.990610e+01 3.990610e+01 4.020742e+01 4.218234e+01 4.218234e+01
## PZA00739.1 PZA01972.14 PZA03012.7 PZA02566.1 PZA01072.1
## 4.218235e+01 4.432735e+01 4.591140e+01 4.591141e+01 4.591141e+01
## PZA02748.3 PZA03638.1 PHM934.19 PZA03639.1 PZA03637.1
## 4.640965e+01 4.640966e+01 4.640966e+01 4.640967e+01 4.640967e+01
## PHM3993.28 PZB02155.1 PZA01038.1 PHM10525.9.11 PZA00118.1.5
## 4.677472e+01 5.115965e+01 5.146422e+01 5.146422e+01 5.176916e+01
## PZA01049.1 PZA01787.1 PHM5468.25 PZA03612.2.1 PHM14152.18
## 5.244158e+01 5.244158e+01 5.244159e+01 5.244159e+01 5.436914e+01
## PZA00766.1 PZA00090.1 PZA02033.1 PHM4203.11 PHM448.23
## 5.533879e+01 5.533880e+01 5.533880e+01 5.533881e+01 5.533881e+01
## PZA00770.1 PZA02011.1 PZA00429.1 PZA03651.1 PZA03650.1
## 5.533882e+01 5.605015e+01 5.829062e+01 5.928465e+01 6.027513e+01
## PHM4757.14 PZA03698.1 PZA03182.5 PZA00951.1 PZA00838.2
## 6.126796e+01 6.271470e+01 6.271471e+01 6.271471e+01 6.347332e+01
## PZA01741.1 PHM15278.6 PHM12749.13 PHM15623.10 PHM3465.6
## 6.347333e+01 6.347333e+01 6.347334e+01 6.864830e+01 6.864831e+01
## PZA00675.1 PZB00811.1 PZA00706.16 PZA02746.2 PHM1834.47
## 7.310476e+01 7.310477e+01 7.310477e+01 7.378847e+01 7.378847e+01
## PZA00460.3.8 PZA00362.1 PZA00020.5 PZA01964.29 PZA00904.1
## 7.760690e+01 7.939066e+01 8.155461e+01 8.187176e+01 8.187177e+01
## PZA01316.1 PHM14046.9 PHM4786.9 PZA00189.23 PHM14104.23
## 8.187177e+01 9.667448e+01 9.667461e+01 9.784082e+01 1.001624e+02
## PZA00760.1 PZA01290.1 PZA02281.3 PZA00071.2 PHM5019.59
## 1.024833e+02 1.037345e+02 1.049805e+02 1.049805e+02 1.049805e+02
## PHM2749.10 PZA02388.1 PZA01600.2 PZA02174.2 PZA01623.3
## 1.049805e+02 1.720588e+02 1.848777e+02 1.848777e+02 1.889861e+02
## PZA00058.1
## 1.889861e+02(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1638.694population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA00368.1 PHM4711.14 PZA00416.7 PZA01601.1 PZA03178.1
## 0.000000e+00 5.000001e-06 1.696468e+00 2.022070e+00 1.060594e+01
## PZA01691.1 PHM9695.8 PZA01951.1 PZA01079.1 PZA02955.3
## 1.126829e+01 1.269407e+01 1.375368e+01 1.760916e+01 1.760916e+01
## PZA01357.2 PZA02528.1 PZA02454.2 PHM1978.111 PZA00758.1
## 1.957802e+01 2.261286e+01 2.293254e+01 2.901766e+01 3.102658e+01
## PHM6428.11 PZA01186.1 PHM2350.17 PZA01209.1 PZA01196.2
## 3.102659e+01 3.102659e+01 3.301611e+01 3.301614e+01 3.301615e+01
## PZA00793.2 PZA02203.1 PZA00498.5 PZA01257.1 PZA01960.1
## 3.301615e+01 3.301616e+01 3.301616e+01 3.423201e+01 3.423202e+01
## PZA00379.2 PHM11114.7 PZA00717.15 PZA01470.1 PZA01297.1
## 3.423202e+01 3.423203e+01 3.423203e+01 3.451877e+01 3.674237e+01
## PZA02522.1 PZA01363.2 PZA02683.1 PZA01301.1 PHM3978.104
## 3.674237e+01 3.674238e+01 3.674238e+01 3.674239e+01 3.940591e+01
## PZA03135.1 PZA00908.2 PZA02019.1 PZA03579.1 PHM4134.8
## 3.940591e+01 3.940592e+01 3.970728e+01 4.168273e+01 4.168274e+01
## PZA00739.1 PZA02566.1 PZA01972.14 PZA01072.1 PZA03012.7
## 4.168274e+01 4.382726e+01 4.382727e+01 4.541069e+01 4.541070e+01
## PHM934.19 PZA02748.3 PZA03637.1 PZA03639.1 PZA03638.1
## 4.590909e+01 4.590909e+01 4.590910e+01 4.590910e+01 4.590911e+01
## PHM3993.28 PZB02155.1 PZA01038.1 PHM10525.9.11 PZA00118.1.5
## 4.627421e+01 5.065887e+01 5.096338e+01 5.096338e+01 5.126826e+01
## PHM5468.25 PZA01787.1 PZA01049.1 PZA03612.2.1 PHM14152.18
## 5.194058e+01 5.194059e+01 5.194059e+01 5.194060e+01 5.386791e+01
## PZA00766.1 PHM448.23 PZA00770.1 PZA00090.1 PHM4203.11
## 5.483654e+01 5.483654e+01 5.483655e+01 5.483655e+01 5.483656e+01
## PZA02033.1 PZA02011.1 PZA00429.1 PHM4757.14 PZA03651.1
## 5.483656e+01 5.554655e+01 5.778220e+01 6.089683e+01 6.089684e+01
## PZA03650.1 PZA03182.5 PZA03698.1 PZA00951.1 PZA00838.2
## 6.089684e+01 6.234117e+01 6.234251e+01 6.234252e+01 6.310055e+01
## PHM15278.6 PZA01741.1 PHM12749.13 PZB00811.1 PHM3465.6
## 6.310056e+01 6.310056e+01 6.310057e+01 6.827337e+01 6.827337e+01
## PZA00675.1 PHM15623.10 PZA00706.16 PHM1834.47 PZA00460.3.8
## 7.272907e+01 7.272908e+01 7.272908e+01 7.341229e+01 7.723004e+01
## PZA02746.2 PZA00362.1 PZA00020.5 PHM4786.9 PZA00904.1
## 7.723005e+01 7.901422e+01 8.117820e+01 8.149536e+01 8.149537e+01
## PZA01316.1 PHM14046.9 PZA01964.29 PHM14104.23 PZA00189.23
## 8.149537e+01 9.628533e+01 9.628534e+01 9.745140e+01 9.745141e+01
## PZA00760.1 PHM2749.10 PZA01290.1 PHM5019.59 PZA02281.3
## 1.023384e+02 1.048949e+02 1.048959e+02 1.048969e+02 1.048969e+02
## PZA00071.2 PZA02388.1 PZA02174.2 PZA01600.2 PZA01623.3
## 1.048969e+02 1.719674e+02 1.847863e+02 1.847863e+02 1.888947e+02
## PZA00058.1
## 1.888947e+02(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -1628.156save.image("population_Z006.RData")Grupo de ligação 7
c <- 7
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA00912.2 PZA02381.1 PZD00055.1 PHM4604.18 PZA01715.1.2
## 0.000000 2.046563 6.488147 6.488152 6.488157
## PZA02197.1 PZB00221.3 PZA00323.3 PZA00511.3 PHM11226.13
## 6.488162 6.488167 6.488172 6.488177 6.930740
## PHM816.29 PZA01369.1 PHM1766.1 PZA02252.2 PZA01096.1
## 7.373281 8.265876 18.886154 18.886159 22.085765
## PZA02111.1 PZA01866.1 PHM4905.6 PZA02397.1 PZA03670.1
## 26.875150 26.875155 26.875160 26.875165 26.875170
## PZA03671.1 PZA02235.14 PHM3330.25 PZA00213.19 PZA00060.2
## 26.875175 26.875180 30.327795 30.657964 31.354653
## PZA00840.1 PZA02325.4 PZA03235.1 PZA02613.1 PZA01819.1
## 31.354658 34.465533 34.465538 34.465543 34.465548
## PZA03470.1 PZB01358.1 PZA00015.5 PZA00152.1 PZA00225.8
## 36.410096 36.869332 36.869337 37.965357 39.467259
## PHM3925.79 PZA00285.3 PHM1218.6 PZA01799.1.2 PZA01386.3
## 102.685330 111.866436 115.158786 115.158791 118.078334
## PZA00466.1 sh1.12.11 PZA01195.3 PHM5181.10 PZA02344.1
## 118.078339 118.078344 118.078349 121.242824 121.242829
## PZA02702.1 PZA03416.7 zb7.2 PHM9374.5 PZA00860.1
## 126.987446 128.593052 129.222920 129.222925 129.222930
## PZA03058.22.21 PZB01110.6 PZB00547.3 PZB00544.2 PZB01042.2
## 129.223447 134.299388 134.299393 134.299398 134.299403
## ZHD1.1 wx1.1 PZA01999.3 PZB00540.3 PZB00959.1
## 134.299408 134.299413 134.299418 134.299423 136.889945
## PZA02648.2 PZA00693.3 PZA03036.6 PZA02878.13 PZA00589.10
## 136.889950 137.240304 137.590516 138.235972 138.235977
## PZA03469.1 PZB00014.1 PZA03057.3 PZA01791.2 PZA00925.2
## 138.235982 138.235987 139.546253 139.546258 139.546263
## PZB00761.1 PZA01861.1 PZA03596.1 PZA01062.1 PZA02545.1
## 140.124987 140.412610 140.995284 140.995289 140.995294
## PZA00947.1 PZA01281.2 PZB01899.1 PHM4303.16 PHM15445.25
## 142.482339 142.482344 143.973911 203.856871 205.900723
## PZA00832.1 PZA00708.3 PHM1911.173 PHM13681.12 PZA03573.1
## 205.900728 207.050036 214.022403 214.022446 222.747497(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1538.77population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA00912.2 PZA02381.1 PZB00221.3 PZA00511.3 PZA00323.3
## 0.000000 2.046563 6.488243 6.488248 6.488253
## PHM4604.18 PZA01715.1.2 PZA02197.1 PZD00055.1 PHM11226.13
## 6.488258 6.488263 6.631429 6.774638 6.927410
## PHM816.29 PZA01369.1 PHM1766.1 PZA02252.2 PZA01096.1
## 7.370682 8.263302 18.881378 18.881383 22.068802
## PZA02235.14 PZA02397.1 PZA03670.1 PZA02111.1 PZA01866.1
## 22.068807 26.959519 26.959524 26.959529 26.959534
## PZA03671.1 PHM4905.6 PHM3330.25 PZA00213.19 PZA00060.2
## 26.959539 26.959544 30.474343 30.809586 31.515313
## PZA00840.1 PZA02613.1 PZA03235.1 PZA02325.4 PZA01819.1
## 31.515318 34.779166 34.779171 34.779176 34.779181
## PZA03470.1 PZB01358.1 PZA00015.5 PZA00152.1 PZA00225.8
## 36.874717 37.376326 37.376331 38.679009 40.633319
## PZB01899.1 PZA01281.2 PZA00947.1 PZA01062.1 PZA03596.1
## 44.987092 46.779576 46.779581 48.508391 48.508396
## PZA02545.1 PZA01861.1 PZB00761.1 PZA03057.3 PZA00925.2
## 48.508401 49.137508 49.445941 50.067088 50.067093
## PZA01791.2 PZA03469.1 PZA02878.13 PZB00014.1 PZA00589.10
## 50.067098 51.451614 51.451619 51.451624 51.451629
## PZA03036.6 PZA00693.3 PZB00959.1 PZA02648.2 PZB01110.6
## 52.132462 52.492720 52.670991 52.851761 52.852717
## wx1.1 zb7.2 PZB00547.3 PZB01042.2 PZB00544.2
## 55.500126 55.500131 55.500136 55.500141 55.500146
## ZHD1.1 PZA01999.3 PZB00540.3 PZA03058.22.21 PHM9374.5
## 55.500151 55.500156 55.500161 55.500166 60.658220
## PZA00860.1 PZA03416.7 PZA02702.1 PZA02344.1 PZA01386.3
## 60.658225 61.290927 62.905395 68.692250 68.692255
## PHM5181.10 sh1.12.11 PZA00466.1 PZA01195.3 PHM1218.6
## 68.692260 71.856585 71.856590 71.856595 71.856600
## PZA01799.1.2 PZA00285.3 PHM3925.79 PHM4303.16 PZA00832.1
## 74.775020 78.066174 87.245598 153.597793 155.645090
## PZA00708.3 PHM15445.25 PHM13681.12 PHM1911.173 PZA03573.1
## 156.796434 156.796439 163.768283 163.768288 172.493422(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -1472.952save.image("population_Z006.RData")Grupo de ligação 8
c <- 8
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA01875.1 PZA02815.25 PHM4468.13 PZA00910.1 PZA02141.1
## 0.000000 1.064806 1.064811 3.616106 9.299753
## PZB01222.1 PZA02688.2 PZA00821.1 PZA00889.2 PHM7922.8
## 10.335187 10.661474 11.319019 13.530160 13.819686
## PZA01468.1 PZA00266.7 PHM4503.25 PZB01569.7 PHM4748.16
## 24.954418 24.954423 24.954428 25.277673 32.732743
## PZA00223.4 PHM5794.13 PZA02436.1 PZA01672.1 PZA03027.12
## 36.440701 36.440706 37.071844 37.071849 37.071854
## PZA01462.1 PZA01342.2 PZA01144.1 PHM11985.27 PZA02247.1
## 37.072066 48.749643 49.681592 49.985589 50.811951
## PZA03102.9 PZB00942.1 PZB01308.1 PZA02148.1 PZA02673.1
## 51.635326 51.976720 51.976725 51.976730 51.976735
## PZA02328.5 PZA02478.7 PZA02187.1.2 PZA00357.19 PZA02262.3
## 53.382015 53.382020 53.382025 53.696075 53.696080
## PZA01884.1 PZB00414.2 PZA01618.2 PZA00473.5 PZA01591.1
## 54.688984 55.333214 57.679363 57.679368 57.679373
## PZA01552.1 PZA01729.1 PHM13020.10 PZA01055.1 lac1.3
## 57.679378 57.994290 57.994295 58.628418 58.628423
## PZA00571.1 PZA00382.17 PZA01736.1 PZA02396.14 PZA01029.1
## 58.628428 58.941328 58.941333 60.926856 60.926861
## PZA02048.2 PZA01589.2 PZA03461.1 PZA00942.2 PZB01658.1
## 60.926866 60.926871 66.836671 74.746601 74.746606
## PHM8909.12 PZA03488.1 PHM15961.13 PZA00158.2 PZA00440.15.1
## 79.631013 79.631018 102.857367 109.339227 111.423654
## PZA03047.12 PZA03063.21 PZA02948.24 PZA03120.1 PZA01901.1
## 111.423659 111.737582 111.737587 112.068039 112.068044
## PZA01509.1 PZA01527.1 PZA01425.2 PZA00427.3 PZA00355.2
## 112.068049 112.690626 115.421779 115.734367 115.734372
## PZA00543.12 PZA03069.8.4 PZA02606.1 PZA00214.1 PHM2551.31
## 116.017859 116.297963 116.583534 118.046066 118.046071
## PZA00006.17 PZB01009.2.1 PZD00072.2
## 118.046076 118.046081 118.046086(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1280.004population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PHM4468.13 PZA01875.1 PZA00910.1 PZA00006.17 PZB01009.2.1
## 0.000000e+00 5.000001e-06 1.448485e+00 6.536965e+01 6.536966e+01
## PZA00214.1 PHM2551.31 PZD00072.2 PHM8909.12 PZA02606.1
## 6.536966e+01 6.536967e+01 6.536967e+01 6.536968e+01 6.683037e+01
## PZA03069.8.4 PZA00543.12 PZA00427.3 PZA00355.2 PZA01425.2
## 6.711560e+01 6.739627e+01 6.767986e+01 6.767987e+01 6.799277e+01
## PZA01527.1 PZA03047.12 PZA03063.21 PZA02948.24 PZA01901.1
## 7.070635e+01 7.175999e+01 7.183803e+01 7.183804e+01 7.213541e+01
## PZA01509.1 PZA03120.1 PZA00440.15.1 PZA00158.2 PHM15961.13
## 7.213542e+01 7.213542e+01 7.275440e+01 7.481938e+01 8.128466e+01
## PZA03488.1 PZA00942.2 PZB01658.1 PZA03461.1 PZA02048.2
## 1.045381e+02 1.094419e+02 1.130662e+02 1.166857e+02 1.226375e+02
## PZA01589.2 PZA02396.14 PZA01029.1 PZA01736.1 PZA00382.17
## 1.226375e+02 1.226375e+02 1.226375e+02 1.246275e+02 1.246275e+02
## PZA00571.1 lac1.3 PZA01055.1 PHM13020.10 PZA01729.1
## 1.249408e+02 1.249408e+02 1.249408e+02 1.255758e+02 1.255758e+02
## PZA01552.1 PZA01618.2 PZA01591.1 PZA00473.5 PZB00414.2
## 1.258912e+02 1.258912e+02 1.258912e+02 1.258912e+02 1.282427e+02
## PZA01884.1 PZA00357.19 PZA02262.3 PZA02328.5 PZB01308.1
## 1.288883e+02 1.298845e+02 1.298845e+02 1.301993e+02 1.316003e+02
## PZA02478.7 PZA02187.1.2 PZA02673.1 PZB00942.1 PZA02148.1
## 1.316003e+02 1.316003e+02 1.316003e+02 1.316003e+02 1.316003e+02
## PZA03102.9 PZA01144.1 PHM11985.27 PZA02247.1 PZA01462.1
## 1.319406e+02 1.338088e+02 1.339718e+02 1.339718e+02 1.352249e+02
## PZA01342.2 PZA01672.1 PZA03027.12 PZA02436.1 PHM5794.13
## 1.352249e+02 1.403847e+02 1.455444e+02 1.455444e+02 1.461837e+02
## PZA00223.4 PHM4748.16 PZB01569.7 PHM4503.25 PZA01468.1
## 1.461838e+02 1.499468e+02 1.575002e+02 1.578259e+02 1.578259e+02
## PZA00266.7 PZA00889.2 PHM7922.8 PZA00821.1 PZA02688.2
## 1.578259e+02 1.685691e+02 1.687816e+02 1.712741e+02 1.719344e+02
## PZA02141.1 PZB01222.1 PZA02815.25
## 1.727208e+02 1.733880e+02 1.813890e+02(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -1422.552save.image("population_Z006.RData")Grupo de ligação 9
c <- 9
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA02365.7 PZA00616.13 PZA01946.7 PZA01690.7 PZA00986.1 PZA02352.1
## 0.000000 1.638188 1.841656 1.841661 2.140298 3.527336
## PZA01714.1 PZA02643.1 PZB00752.1 PZA03583.1 PZA00111.10 PZA00740.3
## 9.494086 9.494091 11.543476 11.543481 12.208108 12.208113
## PZA01542.1 PZD00054.1 PZA02449.13 PZA02984.10 PZA03166.1 PZA03728.1
## 12.208118 12.208123 12.554383 12.900551 14.159505 14.294844
## PZA02854.13 PHM9162.135 PZA00405.7.6 PHM16437.20 PZA02722.1 PZA03176.4
## 14.630991 14.965787 15.263651 19.187208 19.834479 25.450478
## PZA02386.2 PZA02260.2 PHM112.8 PZA00795.1 PHM7898.10 PZA02373.1
## 28.706429 28.706434 28.706439 40.990988 44.998670 44.998675
## PZA01533.2 PZA02223.2 PHM10225.15 PZA00505.6 PZA01802.3 PZA01414.1
## 45.633368 45.633373 45.633378 46.936296 47.792904 48.518255
## PZA00386.4 PZA01028.2 PZA00695.3 PZA01278.2 PZB00605.1 PZA02274.1
## 50.271165 50.271170 53.353541 54.002635 54.002640 68.024225
## PZA00424.1 PZA01744.1 PZA01044.1 PZA01426.1 PZA02035.5 PHM9241.13
## 68.024230 68.024235 68.024240 130.036910 135.761626 147.715931
## PHM3078.12 PZA01909.1.2 PZA02872.1 PHM4080.15 PZA00256.27 PZA03624.1
## 149.882834 152.050777 166.284212 167.848087 169.407528 170.969640
## PZA03344.2 PHM15501.9 PHM4353.31 PZA00132.17 PZA03687.1 PZA00084.2
## 172.345520 174.631133 174.631138 174.631143 174.932768 175.629918
## PZA01936.4 PZA01230.1 PZA02612.1 PZA03363.1 PZA03723.1 PHM12830.14
## 175.629923 175.629928 175.629933 175.629938 175.629943 175.973898
## PZA01210.1.2 PHM904.21 PZA01445.1 PZA03645.1 PZA02291.1 PHM4818.15
## 175.973903 175.973908 175.973913 175.973918 175.973923 175.973928
## PZA00418.2 PZA01607.1 PZA02018.1 PZA01113.1 PZA01933.3 PZA02236.1
## 175.973933 175.973938 176.800129 176.800134 177.638805 178.479773(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1439.1population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA02260.2 PZA00795.1 PZA02373.1 PHM7898.10 PZA02223.2 PHM10225.15
## 0.00000 12.30822 16.31563 16.31564 16.62915 16.94586
## PZA01533.2 PZA00505.6 PZA01802.3 PZA01414.1 PZA00386.4 PZA01028.2
## 18.24663 18.24868 19.10545 19.83083 21.58384 21.58385
## PZA00695.3 PZB00605.1 PZA00424.1 PZA01278.2 PZA02274.1 PZA01044.1
## 24.66623 25.31532 25.31533 25.31533 39.33676 39.33676
## PZA01744.1 PZA01426.1 PZA02035.5 PHM9241.13 PZA01909.1.2 PHM3078.12
## 39.33677 101.33775 107.06253 119.00470 123.55198 123.55198
## PZA02872.1 PZA00256.27 PZA03624.1 PHM4080.15 PZA03344.2 PHM15501.9
## 137.85786 142.84632 142.84632 142.84633 144.20325 146.46451
## PHM4353.31 PZA00132.17 PZA03687.1 PZA01230.1 PZA00084.2 PZA02612.1
## 146.46452 146.46452 146.76362 147.45308 147.45308 147.45309
## PZA03363.1 PZA01936.4 PZA03723.1 PZA02291.1 PZA03645.1 PZA00418.2
## 147.45309 147.45310 147.45310 147.80519 147.80519 147.80520
## PHM4818.15 PZA01445.1 PHM904.21 PHM12830.14 PZA01210.1.2 PZA01607.1
## 147.80520 147.80521 147.80521 147.80522 147.80522 147.80523
## PZA02018.1 PZA01933.3 PZA01113.1 PZA02236.1 PZA02365.7 PZA00986.1
## 148.65583 148.65583 148.65584 150.52316 158.99871 160.82503
## PZA01690.7 PZA00616.13 PZA01946.7 PZA02352.1 PZA03583.1 PZA02643.1
## 160.98218 160.98218 160.98219 162.77795 168.74897 168.74897
## PZA01714.1 PZA01542.1 PZB00752.1 PZD00054.1 PZA00740.3 PZA00111.10
## 168.74898 170.79557 170.79557 171.12525 171.45492 171.45492
## PZA02984.10 PZA02449.13 PZA03728.1 PZA02854.13 PZA03166.1 PHM9162.135
## 171.89080 171.89080 173.30144 173.30144 173.30145 173.98279
## PZA00405.7.6 PHM16437.20 PZA02722.1 PZA03176.4 PZA02386.2 PHM112.8
## 174.27957 178.18530 178.82892 184.46331 187.73542 187.73542(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -1528.935save.image("population_Z006.RData")Grupo de ligação 10
c <- 10
plotRF(population_Z006, chr=c, col.scheme = "redblue")
population_Z006.bb1 <- orderMarkers(cross = population_Z006.bb1, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb1, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb1, chr = c)## PZA02554.1 PZA02527.2 PZA02221.20 PZA01883.2 PZA01313.2 PZA01451.1
## 0.0000 385.6237 771.2474 797.4327 797.4328 797.4329
## PZA02095.10 PHM3631.47 PHM2828.83 PHM3765.7 PZB01301.5 PHM15331.16
## 797.4329 797.4329 797.4329 797.4329 798.9351 800.5173
## PZA00463.3 PHM3896.9 PZA01642.1 PHM3922.32 PZA02961.6 PZA00079.1
## 804.2684 804.9748 804.9748 806.3717 806.3717 807.0921
## PZD00033.3 PZA02470.2 PZA02853.11 PZA03491.1 PZA01597.1 PZA00409.17
## 810.1143 810.1143 810.1143 810.4790 810.4790 810.4790
## PZA00933.3 PZA02941.7 PZA01677.1 PHM2770.19 PZA01877.2 PZB00409.6
## 811.2323 811.2323 811.2323 811.7916 812.3591 813.3439
## PZA00337.4 PZA00814.1 PHM12990.15 PZA01619.1 PHM537.22 PZA00444.1
## 813.8281 814.1725 814.1725 815.0808 816.0712 816.0712
## PZA00400.3 PZA00048.1 PZA01919.2 PZA01292.1 PHM18195.6 PZA02398.2
## 816.1321 816.1321 816.4472 816.4472 816.4472 816.4472
## PHM12625.18 PZA02128.3 PHM4341.42 PZA01089.1 PZA02219.2 PZA03713.1
## 816.4472 816.7672 818.3377 818.3377 819.1275 819.9174
## PZA01141.1 PHM13687.14 PZA03196.1 PZA00866.2 PZA01005.1 PZA00647.9
## 819.9174 819.9174 821.4934 821.8445 821.8445 826.1615
## PZA01241.2 PZA02320.1 PZB01111.8 PZA01456.2 PZA02663.1 PHM15868.56
## 827.7143 829.5817 832.5351 835.9832 838.0331 838.0331
## PHM18513.156 PZA01995.2 PZA03605.1 PZA03604.1 PZA03606.1 PZA03603.1
## 838.0331 841.2003 844.0188 844.0188 844.0188 844.0188
## PZA02969.9 PZA00130.9 PZA03607.1 PZA00007.1 PZA02049.1 PHM5435.25
## 848.9793 848.9793 848.9793 849.5948 849.5948 849.9805
## PZA01001.2 PZA01073.1 PZA02167.2 PZA00062.4 PZA02578.1
## 851.1195 851.1195 851.1195 859.7716 862.1558(loglik.bb1[c] <- attr(population_Z006.bb1$geno[[c]]$map, "loglik"))## [1] -1347.615population_Z006.bb2 <- orderMarkers(cross = population_Z006.bb2, chr=c, use.ripple = FALSE, map.function="kosambi")
plotRF(population_Z006.bb2, chr = c, col.scheme = "redblue")
pull.map(population_Z006.bb2, chr = c)## PZA02554.1 PZA02527.2 PZA02095.10 PZA02221.20 PZA01451.1 PZA01883.2
## 0.0000 385.6237 771.2474 771.2474 797.4285 797.4285
## PHM2828.83 PHM3765.7 PHM3631.47 PZA01313.2 PZB01301.5 PHM15331.16
## 797.4285 797.4285 797.4285 797.4285 798.9308 800.5129
## PZA00463.3 PHM3896.9 PZA01642.1 PHM3922.32 PZA02961.6 PZA00079.1
## 804.2633 804.9698 804.9698 806.3667 806.3667 807.0870
## PZD00033.3 PZA02470.2 PZA02853.11 PZA00409.17 PZA03491.1 PZA01597.1
## 810.1141 810.1141 810.1141 810.4795 810.4795 810.4796
## PZA00933.3 PZA02941.7 PZA01677.1 PHM2770.19 PZA01877.2 PZB00409.6
## 811.2325 811.2325 811.2325 811.7946 812.3587 813.3431
## PZA00337.4 PHM12990.15 PZA00814.1 PZA01619.1 PZA00048.1 PZA00400.3
## 813.8271 814.1713 814.1714 815.0867 815.5335 815.9808
## PZA01292.1 PHM12625.18 PZA00444.1 PZA01919.2 PHM537.22 PZA02398.2
## 816.3144 816.3144 816.3144 816.3144 816.3144 816.3144
## PHM18195.6 PZA02128.3 PHM4341.42 PZA01089.1 PZA02219.2 PZA03713.1
## 816.3144 816.6339 818.1918 818.1918 818.9742 819.7567
## PHM13687.14 PZA01141.1 PZA00866.2 PZA01005.1 PZA03196.1 PZA00647.9
## 819.7567 819.7567 821.7122 821.7122 821.9972 826.6024
## PZA01241.2 PZA02320.1 PZB01111.8 PZA01456.2 PHM15868.56 PHM18513.156
## 828.1374 830.0027 832.9516 836.3967 838.4458 838.4458
## PZA02663.1 PZA01995.2 PZA03605.1 PZA03603.1 PZA03606.1 PZA03604.1
## 838.4458 841.6127 844.4309 844.4309 844.4309 844.4309
## PZA02969.9 PZA00130.9 PZA03607.1 PZA00007.1 PZA02049.1 PHM5435.25
## 849.3917 849.3917 849.3917 850.0072 850.0072 850.3930
## PZA01073.1 PZA01001.2 PZA00062.4 PZA02167.2 PZA02578.1
## 851.5320 851.5321 860.1840 862.5705 862.5705(loglik.bb2[c] <- attr(population_Z006.bb2$geno[[c]]$map, "loglik"))## [1] -1344.009save.image("population_Z006.RData")Ajustes manuais da saída orderMarkers()
Antes de quaisquer ajustes manuais nos pedidos de marcadores, precisamos verificar quais corridas de desempenho melhor para cada grupo de linkage. Para isso, devemos olhar para os comprimentos do grupo de ligação, espaço máximo entre dois marcadores consecutivos (também conhecido como lacuna) e suas probabilidades de log. Maiores probabilidades de log indicam mapas melhores (ou seja, mais prováveis).orderMarkers()
Combinamos as informações da função com a recolhida nos objetos e de cada execução:summaryMap()loglik.bb1loglik.bb1
knitr::kable(cbind(summaryMap(population_Z006.bb1), log.likelihood=c(loglik.bb1, sum(loglik.bb1))))| n.mar | length | ave.spacing | max.spacing | log.likelihood | |
|---|---|---|---|---|---|
| 1 | 175 | 666.0536 | 3.827894 | 385.623712 | -2839.988 |
| 2 | 139 | 150.3227 | 1.089295 | 15.755731 | -1670.498 |
| 3 | 130 | 157.8291 | 1.223481 | 9.164913 | -1926.808 |
| 4 | 127 | 259.1320 | 2.056603 | 81.258438 | -2125.426 |
| 5 | 111 | 140.2493 | 1.274994 | 12.784481 | -1750.669 |
| 6 | 106 | 188.9861 | 1.799868 | 67.078293 | -1638.694 |
| 7 | 85 | 222.7475 | 2.651756 | 63.218071 | -1538.770 |
| 8 | 78 | 118.0461 | 1.533066 | 23.226349 | -1280.004 |
| 9 | 78 | 178.4798 | 2.317919 | 62.012670 | -1439.100 |
| 10 | 77 | 862.1558 | 11.344155 | 385.623712 | -1347.615 |
| overall | 1106 | 2944.0020 | 2.686133 | 385.623712 | -17557.572 |
plotMap(population_Z006.bb1)
save.image("population_Z006.RData")plotRF(population_Z006.bb1, col.scheme = "redblue")
save.image("population_Z006.RData")knitr::kable(cbind(summaryMap(population_Z006.bb2), log.likelihood=c(loglik.bb2, sum(loglik.bb2))))| n.mar | length | ave.spacing | max.spacing | log.likelihood | |
|---|---|---|---|---|---|
| 1 | 175 | 668.1498 | 3.839941 | 385.62371 | -2934.306 |
| 2 | 139 | 149.8523 | 1.085886 | 15.75728 | -1667.511 |
| 3 | 130 | 544.4887 | 4.220843 | 385.62371 | -2089.495 |
| 4 | 127 | 259.6913 | 2.061042 | 81.28966 | -2119.866 |
| 5 | 111 | 146.4435 | 1.331304 | 12.79804 | -1793.540 |
| 6 | 106 | 188.8947 | 1.798997 | 67.07050 | -1628.156 |
| 7 | 85 | 172.4934 | 2.053493 | 66.35220 | -1472.952 |
| 8 | 78 | 181.3890 | 2.355702 | 63.92117 | -1422.552 |
| 9 | 78 | 187.7354 | 2.438122 | 62.00098 | -1528.935 |
| 10 | 77 | 862.5705 | 11.349612 | 385.62371 | -1344.009 |
| overall | 1106 | 3361.7087 | 3.067252 | 385.62371 | -18001.323 |
plotMap(population_Z006.bb2)
save.image("population_Z006.RData")plotRF(population_Z006.bb2, col.scheme = "redblue")
save.image("population_Z006.RData")save.image("population_Z006_bb.RData")Agora, podemos selecionar a melhor ordem até agora, que ainda podemos tentar melhorar fazendo ajustes manuais para cada grupo de linkage. Como estratégia para melhorar o pedido de marcadores, vamos encontrar onde estão as principais lacunas, e corrigi-lo movendo o bloco de marcadores para sua posição mais provável quando olhar para o mapa de calor.
E podemos continuar fazendo isso por cada grupo de ligação. No entanto, outra alternativa oferece melhor encomenda em geral de forma muito mais eficiente.
Escala Multidimensional (MDS) por MDSMap + MAPpoly
E podemos continuar fazendo isso por cada grupo de ligação. No entanto, outra alternativa oferece melhor encomenda em geral de forma muito mais eficiente.
library(mappoly)getMDSorder <- function(cross, chr){
markers <- match(names(cross$geno[[chr]]$map), colnames(cross$rf))
mat <- cross$rf[markers,markers]
rec.mat <- lod.mat <- matrix(rep(NA, length(markers)^2), nrow = length(markers))
colnames(rec.mat) <- colnames(lod.mat) <- rownames(rec.mat) <- rownames(lod.mat) <- colnames(mat)
lod.mat[upper.tri(lod.mat)] <- mat[upper.tri(mat)]
lod.mat[lower.tri(lod.mat)] <- t(lod.mat)[lower.tri(lod.mat)]
rec.mat[lower.tri(rec.mat)] <- mat[lower.tri(mat)]
rec.mat[upper.tri(rec.mat)] <- t(rec.mat)[upper.tri(rec.mat)]
input.mat <- NULL
input.mat$rec.mat <- rec.mat
input.mat$lod.mat <- lod.mat
mds.map <- mappoly::mds_mappoly(input.mat)
mds.ord <- match(as.character(mds.map$locimap$locus), colnames(mat))
return(mds.ord)
}Criaremos um novo objeto chamado que é uma cópia do nosso objeto cruzado original, para que possamos atualizar o pedido apenas dentro. Além disso, criaremos um objeto vazio chamado para armazenar a probabilidade de registro dos pedidos obtidos usando MDS:population_Z006.mds, loglik.mds
Grupo de ligação 1
population_Z006.mds <- population_Z006
loglik.mds <- c()c <- 1
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.68318
## Mean Nearest Neighbour Fit: 4.36971population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA03613.1 PZA01271.1 PZA02129.1 PZA02032.1 PHM2244.142
## 0.0000000 0.0000005 2.5282661 3.4054769 9.0730801
## PZA02372.1 PHM6238.36 PZA00181.2 PZA00528.1 PZA00175.2
## 11.5666722 12.9262457 14.7638228 14.7638233 14.7638238
## PZA00447.8 PZA02284.1 PZA00731.7 PZA00566.5 PZA00106.10
## 19.6939074 19.6939079 19.6939084 21.7818794 22.7099972
## PZA03521.1 PZA00887.1 PZA03551.1 csu1171.2 PZA01497.1
## 22.7099977 22.7099982 24.8408474 31.2601476 31.2601481
## PZA01652.1 PZA02094.9 PZA02393.2 PZB00648.5 PZB00718.5
## 31.2601486 31.2601491 35.0118887 36.5890863 36.5890868
## PZA01030.1 PZA00425.11 PHM13619.5 PZA02487.1 PHM3226.15
## 38.4494562 46.4399030 46.4399035 49.7022766 49.7022771
## PHM4531.46 PZB01957.1 PZA02490.1 PZB02058.1 PZA01348.1
## 49.7022776 49.7022781 54.4478392 54.4478397 54.4478402
## PZB01662.1 PZA01455.1 PZA02686.1 PZA02271.1 PZA02195.1
## 54.4478407 54.4478412 54.4478417 58.9373794 59.2623925
## PZA00240.6 PHM3726.129 PZA00962.1 PZA02376.1 PZA03742.1
## 62.3224685 62.3224690 62.6720715 64.0622494 64.0622499
## PZA03243.2 PZA00081.18 umc13.1 PZA03183.5 PZB00872.3
## 64.0622504 65.0198930 65.6367743 65.9589642 65.9590483
## PHM4913.18 PZA02292.1 PZA03168.5 PZA02737.1 PZA02550.1
## 67.7825758 67.7825763 67.7825768 71.6844270 72.0538764
## PZA02114.1 PZB01062.3 PZA03561.1 PZA01315.1 PZA01476.1
## 72.3929764 72.3929769 72.3929774 73.3581948 73.6733659
## PZA00294.22 PZA03189.4 PHM5098.25 PZA01267.3 PZA00752.1
## 74.6808263 75.0328079 81.8706476 81.8706481 83.9793935
## PZA01135.1 PZA03240.1.2 PZA03465.1 PZA00944.1.2 PZB01235.4
## 85.5832681 88.5922178 88.9403654 88.9403659 91.1611242
## PZA02763.1 PZA00939.1 PHM9418.11 PZA02750.3 PZA01254.2
## 93.4463059 93.4463064 93.4463069 93.4463074 93.4463079
## PZA02070.1 csu1138.3.4 PZA02577.1 PZA03200.2 PZA02135.2
## 94.2117952 94.9131166 94.9131171 95.2592856 100.9524308
## PZA02741.1 an1.5 PZA00455.14.16 PZA02191.1 PHM1968.22
## 100.9524313 100.9524318 104.9853948 109.1072122 109.1072127
## PZA00068.1 PZA03531.1 PZA00619.3 PZA02467.10 PZA03074.27
## 109.7473717 109.7473722 109.7473727 123.3757522 127.7732373
## PZA01216.1 PZA00131.15 PZA01391.1 PHM5480.17 PZA03194.1
## 129.9270022 129.9270027 129.9270032 129.9270037 129.9270042
## PZA01963.15 PZA01019.1 PZA03193.2 PHM12706.14 PZA01039.1
## 129.9270047 129.9270052 137.9938812 138.5903335 138.5903340
## PZA02014.3 PZA03265.3 PHM6043.19 PZA03741.1 PHM5484.22
## 138.5903345 138.5903350 141.6175165 142.2431183 145.6438131
## PHM15871.11 PZA02117.1 PZA02823.1 PZA00658.21 PHM2478.22
## 145.6438136 147.1558071 147.7518395 147.7518400 147.7518405
## PHM4942.12 umc128.2 PZA00664.3 PZA02186.1 PZB01647.1
## 147.7518410 147.7518415 147.7518420 148.8591554 148.8591559
## PZA03001.15 PZA00381.4 PZA03301.2 PHM4926.16 PZA03064.6
## 148.8591564 156.6769060 156.6769065 156.6769070 161.5703925
## PHM16605.19 PZA03404.1 PZA02269.3.4 PHM3034.3 kip1.3
## 162.2774938 164.4884317 164.4884322 167.3612058 167.3612063
## PHM14475.7 glb1.2 PZA01588.1 PZA00339.4 PZA01921.20.19
## 168.4749388 174.5347258 174.5347263 174.5347268 174.5347273
## PHM5526.25 PZA02985.5 PZA03457.1 PZB00008.1 PZB00895.1
## 174.5347278 174.5347283 175.6972680 175.6972685 175.6972690
## PZB00063.1 PZA02278.1 PZA02698.3 PZA00030.11 PZA02520.1
## 175.6972695 182.7205995 185.0889083 185.0889088 189.8383220
## PZA01978.23 PZB00114.1 PZA02204.1 PZA00610.16 PZA03188.3
## 191.6500379 191.6500384 192.7755002 195.5249503 196.4082510
## PZA02957.5 PZA03020.8 PZA00978.1 PZA01246.1 PZA02087.2
## 198.9147055 199.5028446 199.5028451 199.7950403 199.7950408
## PZA00245.20 PHM18705.23 PZB01403.1 PZA00894.7 PZA03037.2
## 199.7950413 201.6342822 201.9900286 203.2470124 203.5587641
## PZA03305.7.1 PZB01227.6 PZA02044.1 PZA00276.18 PZA00307.14
## 203.8926074 203.8926079 220.2955401 227.1766648 227.1766653
## PZA00991.2 PZA00235.9 PZA00623.3 PZA02359.10 PHM9807.9
## 227.1766658 228.3732152 230.0090612 231.1149891 231.1149896
## PZA01238.1.2 PZA01068.1 PZA00343.31 PHM1275.22 PZA00856.2
## 231.4744031 231.4744036 231.9006103 232.9730657 232.9730662
## PZA00243.25 PHM7616.35 PZA00432.4 PZA01239.2 PZA01807.1
## 232.9730667 236.4643858 236.4643863 236.4643868 236.4643873(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -2724.735Grupo de ligação 2
c <- 2
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.74766
## Mean Nearest Neighbour Fit: 4.97056population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA02769.1 PZA01060.1 PZA02480.1 PZA02390.1 PZA01140.1
## 0.0000000 0.0000005 0.0000010 0.0000015 18.1981692
## PZA01680.3 PZA01259.1 PZA00836.1 PZA02015.11 PZA03167.5
## 35.7900461 35.7900466 35.7900471 35.7900476 35.7900481
## PZA00545.26 PZA02068.1 PZA00980.1 PZA00963.3 PHM3512.186
## 35.7900486 35.7900491 35.7900496 51.5700720 53.9956661
## PZA00395.2 PZA02667.1 PZB00765.1 PZA02060.1 PZA02513.1
## 55.2262937 55.2262942 55.2262947 55.2262952 55.2262957
## PZA01265.1 PZA02820.17 PZA01142.4 PZA03024.16 PZA00652.17
## 55.5351997 55.5352002 57.2550690 57.6004375 57.9448403
## PHM532.23 PZA02411.3 PZA03317.1 PZA03172.3 PZA01575.1
## 61.7762813 61.7762818 61.7762823 61.7762828 61.7762833
## PZA02383.1 PZA03452.6 PHM5296.6 PZA02633.4 PZA03324.1
## 61.7762838 61.7762843 61.7762848 61.7762853 61.7762858
## PZA02408.2 PHM1899.157 PZA01304.1 PZA02209.2 PZA02356.7
## 61.7762863 61.7762868 61.7762873 61.7762878 61.7762883
## PZA03320.6 PZA02426.1 PZA02751.1 PZA00352.23 PZA03714.1
## 61.7762888 61.7762893 61.7762898 61.7762903 62.5757105
## PZA03717.1 PZA01410.1 PZA00987.1 PZA02040.2 PZA00300.14
## 62.5757110 66.0365026 66.4144766 66.4144771 66.4144776
## PZA00255.14 PZA02641.2 PZA01294.2.1 PZA03536.1 PZA01763.2
## 67.8225561 67.8225566 67.8225571 67.8225576 68.4582310
## ae1.8.7 PZA02981.2 PZA00148.3 PZA01796.1 PZA01608.1
## 68.4582315 68.4582320 71.0522987 72.8542427 73.5030441
## PZB01017.1 PZA00067.10 PZA01365.1 PZA02164.16 PZA00881.1
## 73.5030446 75.6327253 76.7125500 79.7240474 79.7240479
## PZA00643.13 PZA03049.24 PZA01693.1 PZA00273.5 PZA01779.1
## 79.7240484 81.0566420 82.3951081 82.3951086 84.0633272
## PZA02818.6 PZA02862.3 PZA00261.6 PZA01303.1 PHM5798.39
## 84.4234442 84.4235197 86.0610758 86.0610763 86.9818596
## PZA03677.1 PZB01112.1 PZA02525.1 PZA01349.2 PZA01050.1
## 86.9818601 86.9818606 86.9818611 86.9818616 86.9818621
## PHM4165.14 PZB00232.2 PHM3171.5 PZB01115.3 PZA02676.2
## 86.9818626 88.1861214 88.5208458 88.5208463 89.1888556
## PZA03451.5 PHM1870.20 PZA00222.7 PZA01804.1 PZA00805.1
## 90.2442897 90.2442902 90.2442907 90.2442912 90.2442917
## PZA00522.12.7 PHM3691.18 PZA01530.1 PZA00499.3 PZA00801.1
## 90.2442922 90.2442927 90.2442932 90.2442937 90.2442942
## PZA00996.1 PHM12992.5 PHM4647.8 PZB00869.4 PZA02207.1
## 90.2442947 90.2442952 90.2442957 90.2442962 90.2442967
## PZA00981.3 PHM16854.3 PZA01563.1 PZA02113.1 PZA00934.2
## 90.2442972 90.2442977 90.2442982 98.4810592 98.4810597
## PHM565.31 PZA01427.1 PZA02792.26.25 PZA03274.4 PZA03226.3
## 98.4810602 98.4810607 103.4035967 105.3393843 105.3393848
## PZA00517.7 PZA01523.1 PZA03578.1 PZA01327.1 PZA00985.1
## 105.3393853 107.1707179 107.1707184 109.0971452 112.2322081
## PZA00112.5 PZA03092.7 PZA01284.6 PZB00079.4 PZA00865.1
## 113.4066797 115.3383129 115.3383134 117.0039349 124.1989697
## PZA01371.1 PZA01925.1 PZA02029.21 PZB00094.1 PHM3137.17
## 124.5634584 124.5634589 124.5634594 124.5634599 127.7957179
## PZA02753.1 PZB00054.3 PZA02462.1 PZA02653.12 PHM13122.43
## 131.1684465 131.1684470 131.6043391 135.4225635 143.5416261
## PZA01570.1 PHM5359.10 PZA02316.22 PZA01438.1 PZA00191.5
## 148.5527058 151.3497496 151.3497501 151.3497506 151.3497511
## PZA00818.1 PZA02367.1 PZA01983.1 PZA01887.1
## 159.9400969 160.2288864 160.2288869 160.5080112(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1662.614Grupo de ligação 3
c <- 3
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.19014
## Mean Nearest Neighbour Fit: 3.79384population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA00309.1 PZA02090.1 PZD00038.2 PZA02678.1 PZA00100.10
## 0.000000 9.999378 18.740015 18.740016 19.066814
## PHM12859.7 PZA03527.1 PZA03212.3 PZA00749.1 PZB01944.1
## 20.979107 20.979107 24.374767 24.717404 28.016456
## PZA02098.2 PZA01765.1 PZA00508.2 PHM4204.69 PHM4145.18
## 28.016456 32.773358 32.773358 40.427878 49.699320
## PHM2343.25 PZA03054.5 PZA00210.1.9 PZA01473.1 PZA02427.1
## 53.739350 53.739350 53.739351 53.739351 53.739352
## PZA00348.11 PZA02255.2 zb21.1 PZA00297.2 PZA00380.10
## 53.739352 53.739353 53.739353 54.121361 54.121361
## PHM13823.7 PZA01114.2 PZA03070.9 PHM15899.9 PZA00581.3
## 54.121362 54.121362 54.121363 54.121363 55.011455
## PHM15474.5 PZA03119.1 PZA00279.2 PZA01447.1 PZA02589.1
## 55.011485 55.011485 55.011486 55.011486 55.448689
## PZA00509.1 PZA00265.6 PZA02699.1 PZA02296.1 PZA02742.1
## 55.448689 55.448690 55.448690 56.593880 56.593880
## PHM5502.31 PZA02134.3 PZA02645.2 PZA00707.9 PZA02619.1
## 56.593881 56.593881 56.593882 56.593882 57.537616
## PZA03198.3 PHM15449.10 PZA00413.20.18 PZA02299.16 PZA00363.7
## 59.311944 60.085203 60.085203 60.782189 62.279624
## PZD00016.4 PZD00015.5 PZB02002.1 PHM1745.16 PZA02474.1
## 62.279625 62.279625 62.279626 62.279626 64.910472
## PZA00920.1 PHM890.20 PZB02044.1 PZB02122.1 PHM4955.12
## 65.337858 65.337865 65.337865 65.337866 65.995357
## PZA01934.6 PZA00827.1 PZA00948.1 PZA00828.2 PZB02179.1
## 65.995358 66.744845 67.492249 68.042147 68.042148
## PHM9914.11 PZA00667.2 PZA01396.1 PHM4621.57 PZA00186.4
## 68.042148 71.640208 72.881835 74.040436 74.040437
## PHM2885.31 PZA03073.28.26 PHM1959.26 PZD00027.2 PZA02402.1
## 74.040437 75.203480 76.365095 76.365096 76.365096
## PZA03032.19 PZA00783.1 PZA01726.1 PZA02212.1 PZA01962.12
## 77.413305 77.722749 79.688096 80.274608 87.429488
## PZA02654.3 PHM17210.5 PHM1675.29 PZA03191.1.4 PZA03733.1
## 87.429488 87.429489 92.012925 100.054328 100.054329
## PZA03735.1 zb27.1 PZA00494.2 PZA03647.1 PZA01228.2
## 100.054329 100.054330 102.183136 102.183137 102.183137
## PHM824.17 PZA03743.1 PZA03744.1 PHM13673.53 PZA03255.1
## 102.183138 102.183138 102.183139 102.183139 103.478439
## PZB01109.1 PZA01035.1 PZA00308.24 PZA01457.1 PZA02122.9
## 105.531260 105.531260 105.531261 105.531261 108.301748
## PZA01501.1 PZA00892.5 PZA03154.4 PZA02733.1 PZA00538.18.15
## 108.635705 110.543503 113.087199 113.087200 119.514707
## PZA02516.1 PZA02616.1 PZB01457.1 PZA03146.4 sh2.21
## 123.453325 125.496813 125.496813 132.905698 133.543260
## PZA00750.1 PZA01154.1 PHM3342.31 PZA02665.2 PZA01233.1
## 133.803869 133.803869 138.890874 138.890874 138.890875
## PZA02514.1 PZA02824.4 PZA02668.2 PHM2672.19 PZA00219.7
## 138.890875 138.890876 140.120492 141.994890 141.994891
## PZA03391.1 PZA00402.1 PZA00316.10 PZA01360.3 PZA01688.3
## 141.994891 146.150859 153.092581 154.067433 154.067433
## PHM3852.23 PZA02182.1 PHM2423.33 PZA02423.1 PZA00088.3
## 154.067434 155.078007 163.706763 166.154401 166.154401(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1925.615Grupo de ligação 4
c <- 4
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.78854
## Mean Nearest Neighbour Fit: 5.83293population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA00365.2 PHM5817.15 PZA00680.3 PHM1511.14 PZA00525.17 PHM13440.13
## 0.0000000 0.0000005 0.0000010 4.3269196 4.3269201 4.3269206
## PZA02133.10 PZA02681.8 PZA02175.1 PZA00902.1 PZA02264.5 PZB01233.1
## 4.3269211 5.2273422 5.2273427 5.7002888 6.1729953 9.2529517
## PZA01211.1 PZA00172.12 PZA00613.22 PZA02208.1 PZA02081.1 PZA01935.10
## 11.6054142 15.0162494 20.7955479 20.7955484 20.7955489 23.4537275
## PZA03699.1 PZA03747.1 PZB00901.3.4 PZA02272.3 PHM5822.15 zfl2.9
## 25.4914113 25.4914118 32.5992946 33.3699461 34.9575027 42.1820901
## PZA00108.4 PZA01753.1 PZA02337.4 PZA03559.1 PZA02417.2 PZA00497.4
## 46.3371240 46.3371245 46.9437298 46.9437303 46.9437308 57.3257135
## PZA03228.4 PZA03634.1 PHM6111.5 PZA00590.1 PZA01879.1 PZA03142.5
## 58.2613263 59.2590234 60.7459197 60.7459202 61.1038256 62.9867134
## PHM1962.33 PZA02080.1 PZA01755.1 PZA03568.1 PZA01374.1 PHM4586.12
## 66.2626294 66.2626299 66.2626304 66.2626309 67.8117715 69.2485990
## PZA01336.1 PZA02058.1 PZA01993.7 PZA02378.7 PZA02496.1 PHM10404.8
## 69.2485995 70.1206689 71.2283521 71.2283526 71.2283531 71.2283536
## PZB00183.4 PZA02450.1 PZA01820.1 PZA02774.1 PZA03629.1 PZA02168.1
## 74.4197451 76.5405275 76.5405280 77.6801163 79.7154619 79.7154624
## PZA02279.1 PZA00635.7 PHM3457.6 PHM10321.11 PZA01902.1 PHM4880.179
## 83.7549941 83.7549946 83.7549951 86.2721952 86.2721957 86.2721962
## PHM13360.13 PZA02549.3 PZA00485.2 PHM3626.3 PZA00029.17 PZA02626.1
## 86.2721967 86.2721972 86.2721977 89.7476103 89.7476108 89.7476113
## PZA03211.6 PZA01280.2 PZA01537.2 PZA02939.10 PZA01232.1 PZA01321.1
## 89.7476118 90.3854580 91.0092256 91.3241212 91.3241217 92.7181651
## PZA02465.1 PZA02371.6 PZA00515.10 PZA00637.6 vdac1a.1 PZA03692.1
## 92.7181656 93.7482804 93.7482809 93.7482814 93.7482819 93.7482824
## PZA01638.1 PZA03644.1 PZA00495.5 PZA03659.1 PZA00224.4 PZA03184.2
## 93.7482829 93.7482834 93.7482839 96.0228644 100.8426031 100.8426036
## PZA00755.2 PZA01735.1 PZA03529.1 PZA02890.4 PHM3055.9 PHM3668.12
## 100.8426041 100.8426046 109.1372945 115.0743026 115.0743031 115.0743036
## PZA00824.2 PZA02017.1 PHM7953.11 PZA00803.3 PHM16125.47 PZA02731.1
## 115.0743041 115.0743046 115.0743051 115.0743949 115.3700171 115.3700176
## PZA02329.2 PZA03165.1 PZB01103.2 PHM14412.4 PZA00390.7 PZA01885.2
## 115.3700181 115.3700186 118.7804634 118.7804639 118.7804644 118.7804649
## PZA02077.1 PZA02964.7 PZB00772.7 PZA03602.1 PZA02456.1 PZA00804.1
## 118.7804654 118.7804659 118.7804664 123.8478957 124.9890404 126.1679016
## PZA02680.1 PZA02471.5 PZA02418.2 PZA00527.10 PZA02012.7 PZA02453.1
## 127.0492138 127.9142884 127.9142889 127.9142894 137.4811510 137.4811515
## PZA00163.4 PZA01991.3 PZA02564.2 PZB01013.1 PZA02266.3 PZA01895.1
## 140.5428911 140.8332408 140.8332413 141.7699165 144.6825217 145.3594199
## PZA01352.5 PZA02727.1 PZA02170.1 PHM3094.23 PZA03321.4 PZA03577.1
## 146.7738498 147.1225370 161.6770781 164.8037277 164.8037282 166.2233389
## PZD00022.5
## 167.3335640(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1981.645Grupo de ligação 5
c <- 5
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.6651
## Mean Nearest Neighbour Fit: 4.55648population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA00282.19 PZA00682.17.2 PZA02239.12 PZA01905.12 PZA03598.1
## 0.0000000 0.5994799 7.2727835 7.2727840 7.2727845
## PHM4125.11 PZA03322.5.3 PHM2100.21 PHM5665.26 PZA00529.4
## 13.5211834 15.8407787 15.8407792 19.9391547 20.1690866
## PZA02585.2 PHM5599.20 PZA00513.1 PZA02151.3 PZA00694.6
## 20.1690871 20.1690876 26.4480431 26.7354316 30.2365585
## PZA00521.3 PZA01367.2 PZA00878.2 PZA00636.7 PZA03155.3
## 30.8930030 32.6881480 32.6881485 32.6881490 33.1876569
## PZA00155.1 PZA03081.1 PZB01021.1 PZA02479.1 PZA01332.2
## 34.2096247 34.6334159 34.6334164 34.6334169 35.1237590
## PZA02779.1 PZA02614.2 PZA02421.1 PZA01566.1 PZA01810.2
## 35.1237595 35.1237600 35.5304822 35.5304827 35.5304832
## PZA03205.1 PZA01790.1 PHM4348.16 PZA00193.2 PZA00332.5
## 35.9372149 36.2880464 37.7575165 37.7575170 39.0431195
## PZB01461.1 PZA00344.10 PZA00941.2 PZA01954.1 PZA01766.1
## 39.8788723 41.1535260 41.8258187 49.7968669 49.7968674
## PZA02289.2 PZA02194.1 PZA01976.9 PHM3637.14 PZD00030.2
## 49.7968679 49.7968684 49.7968689 49.7968694 51.2061387
## PZA01187.1 PZA03275.4.1 PZA01477.3 PZA01658.1 PZA01681.1
## 52.3035364 57.9138160 59.9283680 59.9283685 59.9283690
## PZA00271.1 PHM3155.14 PZA01289.1 PZA00453.2 PZA03116.1
## 60.2742244 65.5196874 65.5196879 67.0811803 67.0811808
## PZA01926.1 PZA00057.2 PZA02992.15 PZA02982.7 PZA03152.3
## 75.1112194 79.9139224 84.4330776 84.4330781 84.7292862
## PZA02147.1 PZA03459.1 PZA02027.1 fea2.3 PZA00704.1
## 86.4918524 88.1100641 88.4562847 88.4562852 89.1574276
## PZA03409.1 PZB00093.7 PZA03231.1 PZA00104.1 PZA03203.2
## 89.1574281 90.5669816 90.5669821 93.7990508 94.4642667
## PZA03564.1 PZA00218.1 PZA02767.1 PZA03270.2 PZA03597.1
## 95.1297366 95.7952054 95.7952059 95.7952064 95.7952908
## PZA03254.1 PZA03587.1 bt2.7.4 PHM1307.11 PZA01759.1
## 96.1119836 96.1119841 96.4283662 97.0677088 97.0677093
## PHM14055.6 PZA00726.8.10 PZA00445.22 PZA01751.2 PZA03385.1
## 97.0677098 97.0677103 97.3840891 98.0560308 98.0560313
## PZA00541.1 PZA01106.3 PHM13623.14 PZA01713.4 PHM5572.19
## 98.3759844 98.3759849 98.3759854 98.3759859 98.3759864
## PZA03247.1 PZA02705.1 PHM15427.11 PZA02457.1 PZA02002.1
## 98.3759869 100.0722432 100.0722437 103.7507697 104.9354215
## PZA03048.18 PZA01422.3 PZA00139.4 PHM8527.2 PZA02385.6
## 104.9354220 106.4426794 106.4426799 111.4208030 112.3608876
## PZA02138.1 PZA01122.1 PZA02358.1 PZA00975.1 PZA00683.4
## 112.3608881 112.3608886 112.3608891 126.7622978 126.7622983
## PZA00436.7 PHM3301.28 PHM2438.28 PZA02509.15 PHM1184.26
## 131.8018732 133.8214032 142.8475062 147.3096314 147.3096319
## PZA03227.1
## 147.3096324(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1750.665Grupo de ligação 6
c <- 6
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.21975
## Mean Nearest Neighbour Fit: 3.33761population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PHM5019.59 PZA02281.3 PZA00071.2 PZA01290.1 PHM2749.10
## 0.0000000 0.0000005 0.0000010 0.0000015 0.0000020
## PZA00760.1 PHM14104.23 PZA00189.23 PHM14046.9 PHM4786.9
## 2.6240710 2.6240715 7.7485206 8.9267685 8.9267690
## PZA01964.29 PZA00904.1 PZA01316.1 PZA00020.5 PZA00362.1
## 8.9267695 8.9267700 25.8492504 26.1659543 28.3765769
## PZA00460.3.8 PZA02746.2 PHM1834.47 PZA00675.1 PHM15623.10
## 30.1920471 34.1549581 34.1549586 34.8426863 39.4972170
## PZA00706.16 PZB00811.1 PHM3465.6 PZA00838.2 PZA01741.1
## 39.4972175 39.4972180 39.4972185 39.4972190 44.9363785
## PHM12749.13 PZA03698.1 PHM15278.6 PZA00951.1 PZA03182.5
## 45.6996382 45.6996387 45.6996392 45.6996397 45.6996402
## PHM4757.14 PZA03651.1 PZA03650.1 PZA00429.1 PZA02011.1
## 47.1661283 47.1661288 47.1661293 50.3773044 52.6626249
## PZA00766.1 PHM448.23 PZA00770.1 PZA00090.1 PZA02033.1
## 53.3777165 53.3777170 53.3777175 53.3777180 53.3777185
## PHM4203.11 PHM14152.18 PZA01787.1 PHM5468.25 PZA01049.1
## 53.3777190 54.3557357 56.3201852 56.3201857 56.3201862
## PZA03612.2.1 PZA00118.1.5 PHM10525.9.11 PZA01038.1 PZB02155.1
## 56.3201867 56.9970272 57.3028382 57.3028387 57.6082749
## PHM3993.28 PZA03638.1 PZA02748.3 PZA03637.1 PZA03639.1
## 62.1849548 62.5513890 62.5513895 62.5513900 62.5513905
## PHM934.19 PZA03012.7 PZA01072.1 PZA02566.1 PZA01972.14
## 62.5513910 63.0522399 63.0522404 63.0522409 64.6609793
## PZA03579.1 PZA00739.1 PHM4134.8 PZA02019.1 PHM3978.104
## 66.8514287 66.8514292 66.8514297 68.8659793 69.1682653
## PZA00908.2 PZA03135.1 PZA02683.1 PZA02522.1 PZA01297.1
## 69.1682658 69.1682663 69.1682668 71.9027921 71.9027926
## PZA01301.1 PZA01363.2 PZA01470.1 PHM11114.7 PZA01960.1
## 71.9027931 71.9027936 74.1758928 74.4633663 74.4634563
## PZA00379.2 PZA00717.15 PZA01257.1 PZA00793.2 PZA02203.1
## 74.4634568 74.4634573 74.4634578 74.4634583 75.6941118
## PZA01209.1 PZA00498.5 PZA01196.2 PHM2350.17 PZA01186.1
## 75.6941123 75.6941128 75.6941133 75.6941138 77.7232014
## PHM6428.11 PZA00758.1 PHM1978.111 PZA02454.2 PZA02528.1
## 77.7232019 77.7232024 79.7725240 86.2262013 86.5476551
## PZA01357.2 PZA01079.1 PZA02955.3 PZA01951.1 PHM9695.8
## 89.6832284 91.6960181 91.6960186 95.7107287 96.7839618
## PZA01691.1 PZA03178.1 PZA01601.1 PZA00416.7 PHM4711.14
## 98.2333668 98.9015073 108.9566295 109.2983248 111.1591943
## PZA00058.1 PZA00368.1 PZA01623.3 PZA01600.2 PZA02174.2
## 116.4481390 116.4481395 116.4481400 121.1929348 135.6628449
## PZA02388.1
## 135.6628454(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1561.934Grupo de ligação 7
c <- 7
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.19536
## Mean Nearest Neighbour Fit: 4.44413population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA03573.1 PHM13681.12 PHM1911.173 PZA00708.3 PHM15445.25
## 0.000000 9.469463 9.469464 17.470863 18.762702
## PZA00832.1 PHM4303.16 PZA00912.2 PZA02381.1 PZA00511.3
## 18.762703 21.088766 25.212722 27.690309 32.636178
## PHM4604.18 PZA01715.1.2 PZA02197.1 PZB00221.3 PZD00055.1
## 32.636178 32.636179 32.636236 32.636294 32.636351
## PZA00323.3 PHM11226.13 PHM816.29 PZA01369.1 PZA02252.2
## 32.636409 33.082973 33.529753 34.435038 46.323007
## PHM1766.1 PZA01096.1 PZA02235.14 PZA03670.1 PZA03671.1
## 49.607726 49.607727 54.737882 54.737883 54.737883
## PZA02111.1 PHM4905.6 PZA01866.1 PZA02397.1 PHM3330.25
## 54.737884 54.737884 54.737885 54.737885 58.375704
## PZA00213.19 PZA00060.2 PZA00840.1 PZA01819.1 PZA02325.4
## 58.712038 59.422722 59.422722 62.792914 62.792914
## PZA02613.1 PZA03235.1 PZA03470.1 PZA00015.5 PZB01358.1
## 62.792915 62.792915 64.931892 65.435799 66.755215
## PZA00152.1 PZA00225.8 PZB01899.1 PZA00947.1 PZA01281.2
## 66.755216 68.747441 73.288259 75.112118 75.112119
## PZA02545.1 PZA03596.1 PZA01062.1 PZA01861.1 PZB00761.1
## 76.870159 76.870160 76.870160 77.502826 77.812020
## PZA01791.2 PZA00925.2 PZA03057.3 PZA00589.10 PZB00014.1
## 78.436634 78.436634 78.436635 79.839317 79.839318
## PZA03469.1 PZA02878.13 PZA03036.6 PZA00693.3 PZB00959.1
## 79.839318 79.839319 80.524318 80.885628 80.885658
## PZA02648.2 PZB01110.6 PZB00547.3 PZA01999.3 wx1.1
## 81.247101 83.964592 83.964592 83.964593 83.964593
## PZB00544.2 PZB00540.3 ZHD1.1 PZB01042.2 PZA03058.22.21
## 83.964594 83.964594 83.964676 84.220356 84.220357
## zb7.2 PZA00860.1 PHM9374.5 PZA03416.7 PZA02702.1
## 84.220357 89.965996 90.600854 90.600854 92.236220
## PHM5181.10 PZA02344.1 PZA01386.3 PZA00466.1 PZA01195.3
## 98.345805 98.345806 101.604657 101.604658 101.604658
## sh1.12.11 PHM1218.6 PZA01799.1.2 PZA00285.3 PHM3925.79
## 101.604659 101.604659 104.605413 108.002301 118.013860(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1400.083Grupo de ligação 8
c <- 8
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.41463
## Mean Nearest Neighbour Fit: 4.36041population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA02815.25 PZA01875.1 PHM4468.13 PZA00910.1 PZA02141.1
## 0.000000 1.368224 1.368224 3.132220 9.139695
## PZB01222.1 PZA02688.2 PZA00821.1 PHM7922.8 PZA00889.2
## 10.180003 10.505943 11.164875 13.692862 13.903537
## PZA01468.1 PZA00266.7 PHM4503.25 PZB01569.7 PHM4748.16
## 25.704514 25.704515 25.704515 26.029379 34.060133
## PZA00223.4 PHM5794.13 PZA03027.12 PZA02436.1 PZA01462.1
## 37.905432 37.905432 38.540580 38.540581 38.540581
## PZA01672.1 PZA01342.2 PZA01144.1 PHM11985.27 PZA02247.1
## 38.540582 51.566572 52.507074 52.811855 54.519291
## PZA03102.9 PZB00942.1 PZB01308.1 PZA02148.1 PZA02673.1
## 54.519292 54.861450 54.861450 54.861451 54.861451
## PZA02187.1.2 PZA02478.7 PZA02328.5 PZA02262.3 PZA00357.19
## 54.861452 54.861452 56.291408 56.607849 56.607849
## PZA01884.1 PZB00414.2 PZA01552.1 PZA01591.1 PZA01618.2
## 57.615569 58.267058 60.681044 60.681044 60.681045
## PZA00473.5 PZA01729.1 PHM13020.10 PZA00571.1 lac1.3
## 60.681045 60.998440 60.998441 61.639650 61.639651
## PZA01055.1 PZA01736.1 PZA00382.17 PZA02048.2 PZA01589.2
## 61.639651 61.955018 61.955018 63.990695 63.990695
## PZA02396.14 PZA01029.1 PZA03461.1 PZB01658.1 PZA00942.2
## 63.990696 63.990696 70.273129 70.273129 78.853272
## PZA03488.1 PHM8909.12 PZA00214.1 PZB01009.2.1 PZD00072.2
## 84.461261 100.921463 100.921463 100.921464 100.921464
## PHM2551.31 PZA00006.17 PZA02606.1 PZA03069.8.4 PZA00543.12
## 100.921465 100.921465 102.464632 102.761761 103.044189
## PZA00427.3 PZA00355.2 PZA01425.2 PZA01527.1 PZA03120.1
## 103.338498 103.338499 103.662940 106.589116 107.233615
## PZA01901.1 PZA01509.1 PZA03063.21 PZA00440.15.1 PZA02948.24
## 107.233656 107.233656 107.573271 107.573272 107.573272
## PZA03047.12 PZA00158.2 PHM15961.13
## 107.935170 110.033938 117.024573(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1269.307Grupo de ligação 9
c <- 9
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.22509
## Mean Nearest Neighbour Fit: 4.84618population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA01044.1 PZA02274.1 PZA00424.1 PZA01744.1 PZA01278.2 PZB00605.1
## 0.0000000 0.0000005 0.0000010 0.0000015 15.9314176 15.9314181
## PZA00695.3 PZA00386.4 PZA01028.2 PZA01414.1 PZA01802.3 PZA00505.6
## 16.5841595 19.7583385 19.7583390 21.5401174 22.2699384 23.1340657
## PZA01533.2 PHM10225.15 PZA02223.2 PHM7898.10 PZA02373.1 PZA00795.1
## 23.1340662 24.4540164 25.0926366 25.0926371 25.0926376 29.2611598
## PZA02386.2 PZA02260.2 PHM112.8 PZA03176.4 PZA02722.1 PHM16437.20
## 43.0337909 43.0337914 43.0337919 46.4076417 52.3469293 52.9991366
## PZA00405.7.6 PHM9162.135 PZA02854.13 PZA03166.1 PZA03728.1 PZA02984.10
## 57.0887284 57.3881593 58.0795623 58.0795628 58.0798661 59.2181086
## PZA02449.13 PZA00111.10 PZA00740.3 PZD00054.1 PZA01542.1 PZB00752.1
## 59.5712748 59.9245193 59.9245198 59.9245203 59.9245208 60.5932522
## PZA01714.1 PZA03583.1 PZA02643.1 PZA02352.1 PZA00986.1 PZA01946.7
## 60.5932527 60.5932532 62.6804039 69.0446418 70.4919154 70.7988363
## PZA01690.7 PZA00616.13 PZA02365.7 PZA02236.1 PZA01933.3 PZA01113.1
## 70.7989219 71.0214203 72.8710172 82.0578529 83.9594008 83.9594013
## PZA02018.1 PZA01210.1.2 PZA01607.1 PZA01445.1 PZA00418.2 PHM4818.15
## 83.9594018 84.8169833 84.8169838 84.8169843 84.8169848 84.8169853
## PHM12830.14 PZA02291.1 PHM904.21 PZA03645.1 PZA02612.1 PZA03723.1
## 84.8169858 84.8169863 84.8169868 84.8169873 85.1702017 85.1702022
## PZA01936.4 PZA03363.1 PZA01230.1 PZA00084.2 PZA03687.1 PHM15501.9
## 85.1702027 85.1702032 85.1702037 85.1702042 85.8640689 86.1639262
## PZA00132.17 PHM4353.31 PZA03344.2 PHM4080.15 PZA00256.27 PZA03624.1
## 86.1639267 86.1639272 88.4751208 89.8497802 89.8497807 89.8497812
## PZA02872.1 PZA01909.1.2 PHM3078.12 PHM9241.13 PZA02035.5 PZA01426.1
## 95.0834481 111.4033230 116.1538908 116.1538913 129.4943904 135.5417034(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1390.431Grupo de ligação 10
c <- 10
mds.ord <- getMDSorder(cross = population_Z006.mds, chr = c)## Stress: 0.15726
## Mean Nearest Neighbour Fit: 3.37458population_Z006.mds <- switch.order(cross = population_Z006.mds, chr=c, order=mds.ord, maxit = 10000, tol=1e-5)
plotRF(population_Z006.mds, chr=c, col.scheme = "redblue")
pull.map(population_Z006.mds, chr = c)## PZA02527.2 PZA00062.4 PZA02578.1 PZA02167.2 PZA01001.2 PZA01073.1
## 0.00000 13.05977 15.74208 15.74208 15.74208 25.36570
## PHM5435.25 PZA00007.1 PZA02049.1 PZA02969.9 PZA00130.9 PZA03607.1
## 26.53406 26.53407 26.92644 27.54907 27.80973 33.42354
## PZA03604.1 PZA03606.1 PZA03605.1 PZA03603.1 PZA01995.2 PZA02663.1
## 33.42354 33.42354 33.42354 33.42354 36.35372 39.60807
## PHM15868.56 PHM18513.156 PZA01456.2 PZB01111.8 PZA02320.1 PZA01241.2
## 39.60807 39.60807 41.69741 45.26014 48.29973 50.20221
## PZA00647.9 PZA01005.1 PZA00866.2 PZA03196.1 PZA03713.1 PZA01141.1
## 51.77917 56.28237 56.28237 56.63489 56.63489 58.23680
## PHM13687.14 PZA02219.2 PZA01089.1 PHM4341.42 PZA02128.3 PZA01919.2
## 59.03320 59.03320 59.82983 59.82983 61.42730 61.74886
## PZA00444.1 PHM12625.18 PHM18195.6 PZA01292.1 PZA02398.2 PHM537.22
## 61.74886 61.74886 61.74886 61.74886 61.74894 61.74900
## PZA00400.3 PZA00048.1 PZA01619.1 PHM12990.15 PZA00814.1 PZA00337.4
## 62.08440 63.00017 63.00017 63.92807 63.92807 64.27397
## PZB00409.6 PZA01877.2 PZA02941.7 PHM2770.19 PZA00933.3 PZA01677.1
## 64.76102 65.75641 66.91391 66.91391 66.91391 66.91391
## PZA03491.1 PZA01597.1 PZA00409.17 PZD00033.3 PZA02853.11 PZA02470.2
## 67.67358 67.67358 67.67358 68.04093 68.04093 68.04093
## PZA00079.1 PHM3922.32 PZA02961.6 PZA01642.1 PHM3896.9 PZA00463.3
## 71.16624 71.16624 71.89335 73.31298 73.31298 74.02590
## PHM15331.16 PZB01301.5 PZA01451.1 PZA01883.2 PZA02095.10 PHM3765.7
## 77.92549 79.54458 81.08033 81.08033 81.08033 81.08033
## PHM3631.47 PHM2828.83 PZA01313.2 PZA02221.20 PZA02554.1
## 81.08033 81.08033 81.08033 114.80429 115.35936(loglik.mds[c] <- attr(population_Z006.mds$geno[[c]]$map, "loglik"))## [1] -1184.876Ajustes manuais da orderMarkers
knitr::kable(cbind(summaryMap(population_Z006.mds), log.likelihood=c(loglik.mds, sum(loglik.mds))))| n.mar | length | ave.spacing | max.spacing | log.likelihood | |
|---|---|---|---|---|---|
| 1 | 175 | 236.4644 | 1.358991 | 16.402932 | -2724.735 |
| 2 | 139 | 160.5080 | 1.163102 | 18.198168 | -1662.614 |
| 3 | 130 | 166.1544 | 1.288019 | 9.999378 | -1925.615 |
| 4 | 127 | 167.3336 | 1.328044 | 14.554541 | -1981.645 |
| 5 | 111 | 147.3096 | 1.339179 | 14.401409 | -1750.665 |
| 6 | 106 | 135.6628 | 1.292027 | 16.922480 | -1561.934 |
| 7 | 85 | 118.0139 | 1.404927 | 11.887969 | -1400.083 |
| 8 | 78 | 117.0246 | 1.519800 | 16.460201 | -1269.307 |
| 9 | 78 | 135.5417 | 1.760282 | 16.319875 | -1390.431 |
| 10 | 77 | 115.3594 | 1.517886 | 33.723956 | -1184.876 |
| overall | 1106 | 1499.3723 | 1.368040 | 33.723956 | -16851.905 |
plotMap(population_Z006.mds)
plotRF(population_Z006.mds, col.scheme = "redblue")
save.image("population_Z006_mds.RData") Mapeamento QTL
Aqui, estamos usando a linha de raça recombinante (RIL) população Z006, de modo que esses métodos podem não levar aos mesmos resultados em sua população.
library(qtl)
load("population_Z006_mds.RData")summaryMap(population_Z006.mds)## n.mar length ave.spacing max.spacing
## 1 175 236.5 1.4 16.4
## 2 139 160.5 1.2 18.2
## 3 130 166.2 1.3 10.0
## 4 127 167.3 1.3 14.6
## 5 111 147.3 1.3 14.4
## 6 106 135.7 1.3 16.9
## 7 85 118.0 1.4 11.9
## 8 78 117.0 1.5 16.5
## 9 78 135.5 1.8 16.3
## 10 77 115.4 1.5 33.7
## overall 1106 1499.4 1.4 33.7plot.map(population_Z006.mds)
plotRF(population_Z006.mds, col.scheme = "redblue")
Análise de marcadores únicos
Nossa primeira análise envolve testes para associações entre cada marcador e o traço “PlantHeight”. Fazemos isso executando a função com , que significa “regressão de marcador”:scanone()method = “mr”
population_Z006.mr <- scanone(population_Z006.mds, pheno.col = "PlantHeight", method = "mr")Existem funções chamadas e que podem ser aplicadas a qualquer objeto de mapeamento QTL. A função combinada com o argumento mostra a estatística de teste (pontuação LOD) para cada marcador como pontos.plot()summary()plot()type = “p”
plot(population_Z006.mr, type = "p", main = "Análise de Marcador Simples")
summary(population_Z006.mr)## chr pos lod
## PZA03551.1 1 24.8 2.992
## PZA03092.7 2 115.3 0.677
## PZA01765.1 3 32.8 3.823
## PHM3055.9 4 115.1 3.742
## PZA03155.3 5 33.2 2.144
## PHM3993.28 6 62.2 0.535
## PZA01791.2 7 78.4 5.097
## PZA00158.2 8 110.0 0.869
## PZA03687.1 9 85.9 2.283
## PZD00033.3 10 68.0 5.246population_Z006.perm.mr <- scanone(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "mr",
n.perm = 1000, verbose = FALSE)O resumo da função mostra os marcadores com a maior pontuação lod para cada cromossomo:
plot(population_Z006.perm.mr)
Testes de permutação podem fornecer um valor crítico que pode ser usado para declarar QTL. No nosso caso, usamos , o que significa que estamos executando 1.000 permutações:n.perm = 1000
A função mostra o limiar LOD para um determinado summary()
summary(population_Z006.perm.mr, alpha = 0.05)## LOD thresholds (1000 permutations)
## lod
## 5% 3.08Finalmente, podemos aplicar esse limite e ver quantos QTL temos baseado no SMA:
population_Z006.mr.sig <- summary(population_Z006.mr, perms = population_Z006.perm.mr, alpha = 0.05)
knitr::kable(population_Z006.mr.sig)| chr | pos | lod | |
|---|---|---|---|
| PZA01765.1 | 3 | 32.77336 | 3.822740 |
| PZA02890.4 | 4 | 115.07430 | 3.741960 |
| PZA01791.2 | 7 | 78.43663 | 5.097006 |
| PZD00033.3 | 10 | 68.04093 | 5.245950 |
A tabela apresentam os maiores valores de LOD para cada um dos grupos de ligação. Há uma correspondência entre as posições apontadas pelos dois métodos, se nos intervalos a posição não foi a mesma entre os métodos, é possível verificar que são bem aproximadas.
Mapeamento de intervalo
Para executar o IM, precisamos calcular a probabilidade de genótipo QTL dentro de intervalos de marcadores, condicionadas no mapa genético. Fazemos isso usando a função. O argumento define o tamanho da etapa em que a probabilidade precisa ser calculada (no nosso caso, a cada 1 cM):calc.genoprob()step = 1
population_Z006.mds <- calc.genoprob(cross = population_Z006.mds, step = 1)A função para executar O mapeamento de intervalo é - a mesma para análise de marcadores únicos -, mas precisamos definir o método de estimativa como (máxima probabilidade via algoritmo de Maximização de Expectativa) ou (menos quadrados via regressão Haley-Knott). Podemos correr porque é mais rápido e geralmente funciona bem quando comparado com :scanone () em hk method = “hk” em
population_Z006.im <- scanone(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk")
summary(population_Z006.im)## chr pos lod
## PZA03551.1 1 24.8 2.806
## PZA03092.7 2 115.3 0.597
## c3.loc35 3 35.0 3.895
## c4.loc112 4 112.0 3.292
## PZA03155.3 5 33.2 1.996
## PHM3993.28 6 62.2 0.489
## c7.loc79 7 79.0 5.163
## PZA00158.2 8 110.0 0.773
## PZA03687.1 9 85.9 2.523
## PZA03491.1 10 67.7 5.289Para saber o valor crítico para declarar QTL usando permutações, primeiro executamos a função com os argumentos e:scanone () method = “hk” n.perm = 1000
population_Z006.perm.im <- scanone(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk",
n.perm = 1000, verbose = FALSE)Em seguida, a função mostra o limiar LOD para um determinado summary() αα nível (no nosso caso,):alpha = 0.05
summary(population_Z006.perm.im, alpha = 0.05)## LOD thresholds (1000 permutations)
## lod
## 5% 3.1plot(population_Z006.im, col = "red", main = "Mapeamento de Intervalo")
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")
Um enredo para comparar duas abordagens:
plot(population_Z006.mr, population_Z006.im, type = c("p", "l"), col = c("black", "red"), main = "Análise de Marcador Simples vs Mapeamento de Intervalo")
add.threshold(population_Z006.mr, perms = population_Z006.perm.mr, alpha = 0.05, lty = 2, col = "black")
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")
legend("topright", legend = c("AMS", "MP"), lty = c(2, 1), col = c("black", "red"))
Agora, podemos aplicar o limiar ao nosso objeto mapeamento de Intervalo,
que dá uma lista das regiões QTL que são altamente significativas:
population_Z006.im.sig <- summary(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05)
population_Z006.im.sig## chr pos lod
## c3.loc35 3 35.0 3.89
## c4.loc112 4 112.0 3.29
## c7.loc79 7 79.0 5.16
## PZA03491.1 10 67.7 5.29Observe que podemos usar para extrair os cromossomos () e posições () de nossa QTL significativa: population_Z006.im.sig. sig chr pos
chr <- population_Z006.im.sig$chr
pos <- population_Z006.im.sig$posFinalmente, o e são usados para mostrar as estimativas de efeito QTL para uma população RIL com base em um objeto derivado da função: makeqtl() fitqtl() sim.geno()
population_Z006.mds <- sim.geno(cross = population_Z006.mds, step = 1)
population_Z006.im.qtl <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.im.qtl## QTL object containing imputed genotypes, with 16 imputations.
##
## name chr pos n.gen
## Q1 3@35.0 3 35.000 2
## Q2 4@112.0 4 112.000 2
## Q3 7@79.0 7 79.000 2
## Q4 10@67.7 10 67.674 2Se quisermos saber o efeito da QTL, e usarmos a função com o argumento, e, respectivamente:Q1 Q2 Q3 fitqtl() formula = y ~ Q1 formula = y ~ Q2 formula = y ~ Q3
population_Z006.im.fit.Q1 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
formula = y ~ Q1, get.ests = TRUE)
summary(population_Z006.im.fit.Q1)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q1
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 1 3382.399 3382.3986 3.190665 7.635236 0.0001264781 0.0001405089
## Error 183 40917.458 223.5927
## Total 184 44299.857
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.622 1.103 133.822
## 3@35.0 -4.338 1.117 -3.886population_Z006.im.fit.Q2 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
formula = y ~ Q2, get.ests = TRUE)
summary(population_Z006.im.fit.Q2)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q2
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 1 3432.016 3432.0157 3.239408 7.747239 0.000112285 0.0001249311
## Error 183 40867.841 223.3215
## Total 184 44299.857
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.921 1.105 133.918
## 4@112.0 -4.352 1.112 -3.914population_Z006.im.fit.Q3 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
formula = y ~ Q3, get.ests = TRUE)
summary(population_Z006.im.fit.Q3)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q3
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 1 5342.667 5342.6671 5.16286 12.06024 1.08232e-06 1.27848e-06
## Error 183 38957.189 212.8808
## Total 184 44299.857
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.481 1.080 136.568
## 7@79.0 5.442 1.086 5.011population_Z006.im.fit.Q4 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
formula = y ~ Q4, get.ests = TRUE)
summary(population_Z006.im.fit.Q4)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q4
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 1 5124.498 5124.498 4.938514 11.56775 1.852068e-06 2.172548e-06
## Error 183 39175.359 214.073
## Total 184 44299.857
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.598 1.082 136.355
## 10@67.7 5.273 1.078 4.892Estas seriam a expressão exata do modelo de QTL único como o método mapeamento de intervalo é. Ou seja, valores não podem ser somados. Foram estimados de forma independente e, portanto, não contabilizam a covariância entre os efeitos.
Uma maneira melhor de saber a quantidade de variação explicada pelos três QTL é construindo um modelo QTL múltiplo:
population_Z006.im.fit <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.im.qtl,
formula = y ~ Q1 + Q2 + Q3 + Q4, get.ests = TRUE)
summary(population_Z006.im.fit)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q1 + Q2 + Q3 + Q4
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 4 16611.92 4152.9804 18.88034 37.49882 0 0
## Error 180 27687.94 153.8219
## Total 184 44299.86
##
##
## Drop one QTL at a time ANOVA table:
## ----------------------------------
## df Type III SS LOD %var F value Pvalue(Chi2) Pvalue(F)
## 3@35.0 1 2218 3.095 5.006 14.42 0 2e-04 ***
## 4@112.0 1 3595 4.904 8.115 23.37 0 2.85e-06 ***
## 7@79.0 1 5592 7.389 12.622 36.35 0 9.12e-09 ***
## 10@67.7 1 4916 6.565 11.096 31.96 0 6.09e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.4300 0.9117 161.713
## 3@35.0 -3.8401 0.9930 -3.867
## 4@112.0 -4.6228 0.9497 -4.868
## 7@79.0 5.5605 0.9572 5.809
## 10@67.7 4.9275 0.9678 5.092save.image("population_Z006_im.RData")Mapeamento de intervalos compostos
Para executar CIM, existe uma função chamada . Nesta função, precisamos especificar dois argumentos principais: cim()
O número de cofatores ou marcadores covariados: , que podem ser dados como n.marcovar 2×N−−√≈272×N−−√≈27. O tamanho da janela: , que podem ser valores diferentes dependendo do tamanho da população e saturação do mapa. Geralmente vai de 5 a 15 cM, mas pode ir até 20 ou 30 cM, ou tomar todo o cromossomo (ou seja, deixar um cromossomo fora - LOCO). window
population_Z006.cim10 <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.marcovar = 2 *
sqrt(nind(population_Z006)), window = 10)
population_Z006.cim15 <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.marcovar = 2 *
sqrt(nind(population_Z006)), window = 15)
population_Z006.cim20 <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.marcovar = 2 *
sqrt(nind(population_Z006)), window = 20)
population_Z006.cimInf <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk",
n.marcovar = 2 * sqrt(nind(population_Z006.mds)), window = Inf)summary(population_Z006.cim10)## chr pos lod
## c1.loc193 1 193.0 7.162
## PZA01303.1 2 86.1 0.651
## PZA01934.6 3 66.0 6.422
## c4.loc113 4 113.0 11.411
## PZA03048.18 5 104.9 5.628
## PZA02388.1 6 135.7 3.175
## PZA02878.13 7 79.8 14.375
## c8.loc80 8 80.0 7.015
## c9.loc78 9 78.0 6.554
## PZA00647.9 10 51.8 4.168summary(population_Z006.cim15)## chr pos lod
## c1.loc155 1 155.0 7.81
## PZA01284.6 2 115.3 1.76
## PZA00309.1 3 0.0 5.16
## c4.loc113 4 113.0 6.94
## PZA02479.1 5 34.6 7.59
## c6.loc132 6 132.0 4.53
## PZB01042.2 7 84.2 11.75
## c8.loc81 8 81.0 1.04
## c9.loc79 9 79.0 4.17
## c10.loc67 10 67.0 8.19summary(population_Z006.cim20)## chr pos lod
## c1.loc21 1 21.0 4.93
## PZA03092.7 2 115.3 3.29
## c3.loc63 3 63.0 8.68
## c4.loc113 4 113.0 13.51
## PZA00155.1 5 34.2 5.80
## PHM10525.9.11 6 57.3 3.49
## PZB00761.1 7 77.8 16.64
## c8.loc80 8 80.0 4.64
## PZA03687.1 9 85.9 4.15
## PZD00033.3 10 68.0 10.79summary(population_Z006.cimInf)## chr pos lod
## PZA03551.1 1 24.8 6.46
## PZA01265.1 2 55.5 1.20
## c3.loc70 3 70.0 9.60
## c4.loc113 4 113.0 9.46
## c5.loc25 5 25.0 3.59
## c6.loc132 6 132.0 7.30
## PZB00761.1 7 77.8 14.84
## c8.loc77 8 77.0 4.08
## c9.loc77 9 77.0 6.62
## PHM12990.15 10 63.9 13.28A fim de fazer os testes de permutação serem menos conservadores e fazê-los funcionar para qualquer tamanho de janela, podemos escolher . Isso significa que não serão permitidos cofatores com o grupo de vinculação onde os testes estão sendo realizados: window = Inf
set.seed(8617)
population_Z006.perm.cim <- cim(cross = population_Z006.mds, pheno.col = "PlantHeight", method = "hk",
n.marcovar = 2 * sqrt(nind(population_Z006.mds)), window = Inf, n.perm = 1000)Você pode usar mais threads de computador se acontecer de você ter um computador com vários núcleos usando o argumento (por exemplo), para que os testes de permutaion sejam executados mais rápido. n.clustern. cluster = 4
summary(population_Z006.perm.cim, alpha = 0.05)## LOD thresholds (1000 permutations)
## [,1]
## 5% 7.29Agora, se usarmos esses resultados de permutação para identificar o QTL mais significativo, temos:
summary(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)## chr pos lod
## c4.loc113 4 113.0 11.4
## PZA02878.13 7 79.8 14.4summary(population_Z006.cim15, perms = population_Z006.perm.cim, alpha = 0.05)## chr pos lod
## c1.loc155 1 155.0 7.81
## PZA02479.1 5 34.6 7.59
## PZB01042.2 7 84.2 11.75
## c10.loc67 10 67.0 8.19summary(population_Z006.cim20, perms = population_Z006.perm.cim, alpha = 0.05)## chr pos lod
## c3.loc63 3 63.0 8.68
## c4.loc113 4 113.0 13.51
## PZB00761.1 7 77.8 16.64
## PZD00033.3 10 68.0 10.79summary(population_Z006.cimInf, perms = population_Z006.perm.cim, alpha = 0.05)## chr pos lod
## c3.loc70 3 70.0 9.60
## c4.loc113 4 113.0 9.46
## c6.loc132 6 132.0 7.30
## PZB00761.1 7 77.8 14.84
## PHM12990.15 10 63.9 13.28plot(population_Z006.cim10, population_Z006.cim15, population_Z006.cimInf, col = c("blue", "orange", "cyan"), main = "Mapeamento de Intervalo Composto")
add.threshold(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)
legend("topright", legend = c("ws = 10", "ws = 15", "ws = Inf"), lty = 1, col = c("blue",
"orange", "cyan"))
Porque não declaramos a maior parte da QTL que achamos que são verdadeiras, e tem os mesmos resultados que, nós os retiramos da trama. Podemos comparar nossos resultados de CIM quando e com nossos resultados de mapeamento de intervalo: population_Z006cim20 population_Z006.cim15 population_Z006.cimInf. window = Inf
plot(population_Z006.im, population_Z006.cim10, population_Z006.cimInf, col = c("red", "blue", "cyan"), main = "Mapeamento de Intervalo vs Mapeamento de Intervalo Composto")
add.threshold(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")
legend("topright", legend = c("MI", "MIC (ws = 10)", "MIC (ws = Inf)"), lty = 1,
col = c("red", "blue", "cyan"))
Também podemos limitar a visualização a cromossomos onde qtl apareceu nas análises mapeamento de intervalo vs mapeamento de intervalo composto:
plot(population_Z006.im, population_Z006.cim10, population_Z006.cimInf, col = c("red", "blue", "cyan"), chr = c(1, 3, 4, 7, 10), main = "Mapeamento de Intervalo vs Mapeamento de Intervalo Composto")
add.threshold(population_Z006.im, perms = population_Z006.perm.im, alpha = 0.05, col = "red")
add.threshold(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05, col = "black")
add.cim.covar(population_Z006.cimInf, chr = c(1, 3, 4, 7, 10), col = "green")
legend("topleft", legend = c("MI", "MIC (ws = 10)", "MIC (ws = Inf)"), lty = 1, col = c("red",
"blue", "cyan"))
Os pontos verdes representam a localização dos marcadores selecionados como covariados (cofatores) para a pesquisa do mapeamento de intervalo composto com.window = Inf
Notei que o QTL no cromossomo 1 e 3 que identificamos usando mapeamento de intervalo está logo abaixo do limiar para a análise do mapeamento de intervalo composto - por isso os chamamos de QTL suggetive aqui. Dependendo da decisão do pesquisador, poderíamos abaixar um pouco o limiar para incluí-lo como um QTL usando mapeamento de intervalo composto, o que provavelmente está bem. Veremos, no entanto, uma maneira melhor de lidar com isso ao executar um modelo de QTL múltiplo, que é o nosso próximo tópico.
Neste momento, suponhamos que queremos investigar as estimativas de QTL sob mapeamento de intervalo composto. Só precisamos armazenar os cromossomos QTL e posições do nosso modelo selecionado (mapeamento de intervalo composto com window = 10ws=10ws=10):
set.seed(8617)
population_Z006.cim.sig <- summary(population_Z006.cim20, perms = population_Z006.perm.cim, alpha = 0.05)
population_Z006.cim.sigNo entanto, nottei que apenas um QTL no cromossomo 7 está listado (aquele com maior pontuação lod). Precisamos encontrar o outro olhando para as posições naquele cromossomo que tem uma pontuação LOD maior que o nosso limiar de 7.29:
peak.markers <- which(population_Z006.cim10$lod[population_Z006.cim10$chr == 7] > 7.29)
population_Z006.cim10$lod[peak.markers]## [1] 0.3737050 0.4410512 0.4982776 0.5334941 0.5411826 0.4975599 0.4877348
## [8] 0.3925818 0.3925818 0.3925819 0.4570803 0.4778816 0.4719463 0.4684601
## [15] 0.4220755 0.5212033 0.5301639 0.6080766 0.6976733 0.7869117 0.8565369
## [22] 0.8809354 1.4714431 1.5432509 1.5432509 1.6461306 2.4933477 3.2083408
## [29] 3.2420422 4.7817944 5.3837805 5.4329987 5.1992741 4.3416781 0.5180057population_Z006.cim10$pos[peak.markers]## [1] 68.00000 69.00000 70.00000 71.00000 71.68443 72.00000 72.05388 72.39298
## [9] 72.39298 72.39298 73.00000 73.35819 73.67337 74.00000 74.68083 75.00000
## [17] 75.03281 76.00000 77.00000 78.00000 79.00000 80.00000 81.00000 81.87065
## [25] 81.87065 82.00000 83.00000 83.97939 84.00000 85.00000 85.58327 86.00000
## [33] 87.00000 88.00000 88.59222Para mostrar as estimativas de efeito QTL para uma população RIL, precisamos usarmos a função, então e: sim.geno() makeqtl() fitqtl()
population_Z006.mds <- sim.geno(population_Z006.mds, step = 1)
population_Z006.cim.qtl <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.cim.qtl## QTL object containing imputed genotypes, with 16 imputations.
##
## name chr pos n.gen
## Q1 3@35.0 3 35.000 2
## Q2 4@112.0 4 112.000 2
## Q3 7@79.0 7 79.000 2
## Q4 10@67.7 10 67.674 2population_Z006.cim.fit <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.cim.qtl,
formula = y ~ Q1 + Q2 + Q3, get.ests = TRUE)
summary(population_Z006.cim.fit)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q1 + Q2 + Q3
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 3 11885.79 3961.9312 12.54937 26.83032 1.741163e-12 2.986722e-12
## Error 181 32414.06 179.0832
## Total 184 44299.86
##
##
## Drop one QTL at a time ANOVA table:
## ----------------------------------
## df Type III SS LOD %var F value Pvalue(Chi2) Pvalue(F)
## 3@35.0 1 2691 3.204 6.075 15.03 0 0.000148 ***
## 4@112.0 1 3472 4.088 7.837 19.39 0 1.82e-05 ***
## 7@79.0 1 5165 5.939 11.658 28.84 0 2.39e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.457 0.993 148.493
## 3@35.0 -4.034 1.051 -3.839
## 4@112.0 -4.405 1.035 -4.257
## 7@79.0 5.393 1.007 5.358save.image("population_Z006_mds.RData")Teremos maneiras melhores de verificar se há outros QTL nos cromossomos 1 e 3 sob a abordagem do modelo de Mapeamento de Intervalo Múltiplo.
Mapeamento de intervalo múltiplo
O Mapeamento de intervalo Multiplo usa vários intervalos de marcadores simultaneamente para ajustar vários QTL putativos diretamente no modelo para mapeamento de QTL.
Aqui, estou usando a linha de raça recombinante (RIL) população Z006, de modo que esses métodos podem não levar aos mesmos resultados em sua população.
library(qtl)
load("population_Z006_mds.RData")summaryMap(population_Z006.mds)## n.mar length ave.spacing max.spacing
## 1 175 236.5 1.4 16.4
## 2 139 160.5 1.2 18.2
## 3 130 166.2 1.3 10.0
## 4 127 167.3 1.3 14.6
## 5 111 147.3 1.3 14.4
## 6 106 135.7 1.3 16.9
## 7 85 118.0 1.4 11.9
## 8 78 117.0 1.5 16.5
## 9 78 135.5 1.8 16.3
## 10 77 115.4 1.5 33.7
## overall 1106 1499.4 1.4 33.7plotMap(population_Z006.mds)
plotRF(population_Z006.mds, col.scheme = "redblue")
Adicionando os principais efeitos manualmente.
R/qtl tem várias funções para lidar com modelos de QTL múltiplos. Vamos focar em alguns deles que são mais interessantes para este conjunto de dados específico.
makeqtl e fitqtl
Usei os resultados da execução qtl anterior, ou seja, mapeamento de intervalo composto.
Primeiro, podemos dar uma olhada no objeto (anteriormente nomeado ) do mapeamento de intervalo composto com execução: population_Z006.cim10
summary(population_Z006.cim10, perms = population_Z006.perm.cim, alpha = 0.05)## chr pos lod
## c4.loc113 4 113.0 11.4
## PZA02878.13 7 79.8 14.4Notei que quatro picos tinham pontuação lod maior que nosso limiar cim de 7,29. No entanto, apenas um pico foi listado acima (o com a maior pontuação lod). Uma maneira de encontrar as outras posições é olhando para a pontuação máxima de LOD para posições no final do cromossomo 1 (posição > 150 cM):
max(population_Z006.cim10[population_Z006.cim10$chr == 1 & population_Z006.cim10$pos > 150, ])## chr pos lod
## c1.loc193 1 193 7.16chr <- c(1, 1, 4, 7, 10)
pos <- c(24, 192, 113, 77, 68.0)Para construir um modelo de QTL múltiplo, precisamos usar a função, então: calc.genoprob() makeqtl()
population_Z006.mds <- calc.genoprob(cross = population_Z006.mds, step = 1)
population_Z006.qtl <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.qtl## QTL object containing imputed genotypes, with 16 imputations.
##
## name chr pos n.gen
## Q1 1@24.0 1 24 2
## Q2 1@192.0 1 192 2
## Q3 4@113.0 4 113 2
## Q4 7@77.0 7 77 2
## Q5 10@68.0 10 68 2A partir das avaliações consideraremos os seguintes QTLs e suas interações.
plot(population_Z006.qtl)
Então, encaixei esse modelo usando a função: fitqtl()
population_Z006.fit <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl,
formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5, get.ests = TRUE)
summary(population_Z006.fit)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 5 16781.71 3356.3413 19.12744 37.88208 0 0
## Error 179 27518.15 153.7327
## Total 184 44299.86
##
##
## Drop one QTL at a time ANOVA table:
## ----------------------------------
## df Type III SS LOD %var F value Pvalue(Chi2) Pvalue(F)
## 1@24.0 1 1840.7 2.601 4.155 11.974 0.001 0.000675 ***
## 1@192.0 1 599.7 0.866 1.354 3.901 0.046 0.049803 *
## 4@113.0 1 3194.8 4.412 7.212 20.781 0.000 9.51e-06 ***
## 7@77.0 1 5833.1 7.723 13.167 37.943 0.000 4.68e-09 ***
## 10@68.0 1 5312.5 7.091 11.992 34.557 0.000 1.98e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.7239 0.9318 158.538
## 1@24.0 -3.3473 0.9615 -3.481
## 1@192.0 1.7958 0.9238 1.944
## 4@113.0 -4.3115 0.9460 -4.557
## 7@77.0 5.6825 0.9285 6.120
## 10@68.0 5.4466 0.9357 5.821Podemos observar que o QTL no cromossomo 1 e algumas das interações sugeridas não foram significativas ao nível de 5%, portanto podemos desconsiderá-los do modelo.
refineqtl e plotLodProfile
Toda vez que é encaixado um novo modelo de QTL múltiplo, recomenda-se que refinemos as posições de pico QTL porque pode haver alterações ao testar cada QTL condicional ao outro QTL no modelo. Executamos esta tarefa usando a função e plotamos os resultados usando: refineqtl() plotLodProfile()
population_Z006.ref <- refineqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl,
formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5)## pos: 24 192 113 77 68
## Iteration 1
## Q1 pos: 24 -> 155
## LOD increase: 0.914
## Q4 pos: 77 -> 78.43663
## LOD increase: 0.431
## Q5 pos: 68 -> 67.67358
## LOD increase: 0.179
## Q2 pos: 192 -> 191.65
## LOD increase: 0.035
## Q3 pos: 113 -> 114
## LOD increase: 0.127
## all pos: 24 192 113 77 68 -> 155 191.65 114 78.43663 67.67358
## LOD increase at this iteration: 1.687
## Iteration 2
## Q3 pos: 114 -> 114
## LOD increase: 0
## Q4 pos: 78.43663 -> 78.43663
## LOD increase: 0
## Q1 pos: 155 -> 157
## LOD increase: 0.378
## Q5 pos: 67.67358 -> 67.67358
## LOD increase: 0
## Q2 pos: 191.65 -> 191.65
## LOD increase: 0
## all pos: 155 191.65 114 78.43663 67.67358 -> 157 191.65 114 78.43663 67.67358
## LOD increase at this iteration: 0.378
## Iteration 3
## Q2 pos: 191.65 -> 191.65
## LOD increase: 0
## Q1 pos: 157 -> 157
## LOD increase: 0
## Q5 pos: 67.67358 -> 67.67358
## LOD increase: 0
## Q3 pos: 114 -> 115.0743
## LOD increase: 0.242
## Q4 pos: 78.43663 -> 78
## LOD increase: 0.307
## all pos: 157 191.65 114 78.43663 67.67358 -> 157 191.65 115.0743 78 67.67358
## LOD increase at this iteration: 0.55
## Iteration 4
## Q4 pos: 78 -> 78
## LOD increase: 0
## Q1 pos: 157 -> 157
## LOD increase: 0
## Q5 pos: 67.67358 -> 67.67358
## LOD increase: 0
## Q2 pos: 191.65 -> 191.65
## LOD increase: 0
## Q3 pos: 115.0743 -> 115.0743
## LOD increase: 0
## all pos: 157 191.65 115.0743 78 67.67358 -> 157 191.65 115.0743 78 67.67358
## LOD increase at this iteration: 0
## overall pos: 24 192 113 77 68 -> 157 191.65 115.0743 78 67.67358
## LOD increase overall: 2.615plotLodProfile(population_Z006.ref)
Em seguida, foi feito outra rodada de testes atualizados para as novas posições: fitqtl()
population_Z006.fit2 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref,
formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5, get.ests = TRUE)
summary(population_Z006.fit2)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 5 18515.63 3703.125 21.74195 41.79613 0 0
## Error 179 25784.23 144.046
## Total 184 44299.86
##
##
## Drop one QTL at a time ANOVA table:
## ----------------------------------
## df Type III SS LOD %var F value Pvalue(Chi2) Pvalue(F)
## 1@157.0 1 3045 4.485 6.874 21.141 0.000 8.04e-06 ***
## 1@191.7 1 1417 2.149 3.198 9.834 0.002 0.002 **
## 4@115.1 1 3055 4.498 6.896 21.208 0.000 7.79e-06 ***
## 7@78.0 1 6987 9.633 15.773 48.508 0.000 6.04e-11 ***
## 10@67.7 1 6297 8.778 14.215 43.718 0.000 4.22e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.6114 0.8942 165.078
## 1@157.0 -4.2558 0.9833 -4.328
## 1@191.7 2.9112 0.9456 3.079
## 4@115.1 -4.1713 0.9227 -4.521
## 7@78.0 6.2472 0.9320 6.703
## 10@67.7 5.7798 0.9398 6.150scantwo
Para adicionar efeitos principais ou epistáticos, precisamos descobrir qual é a “regra de parada”. Em R/qtl, tal regra é dada por pontuação de LOD penalizada para um modelo com mais de um QTL (função ) fornecido por permutações (). Esta função leva muito tempo para ser executada: scantwon.perm = 1000
set.seed(8617)
permo.2dim <- scantwo(population_Z006.mds, pheno.col = "PlantHeight", method = "hk", n.perm = 1000)## Doing permutation in batch mode ...save(permo.2dim, file = "permo_2dim.RData")É sempre bom salvar saídas de permutação devido ao tempo que essas funções levam para serem executadas, para que você possa usar os resultados para a mesma característica mais tarde.
Agora, podemos ver um resumo do objeto e calcular as penalidades que serão usadas para pesquisa automática de modelos de vários QTL.
load("permo_2dim.RData")
summary(permo.2dim, alpha = 0.05)## (1000 permutations)
## full fv1 int add av1 one
## 5% 6.49 4.83 4.15 5.19 2.88 3.04penalties <- calc.penalties(permo.2dim)
penalties## main heavy light
## 3.037694 4.154428 1.797158Limiares derivados de permutações (ou seja, para um escaneamento bidimensional do genoma de dois QTL) são usados para calcular penalidades sobre os principais efeitos e interações. Olhe para saber mais sobre as penalidades. Você também pode encontrar uma explicação detalhada sobre este critério por Manichaikul et al. (2009). scantwo help(calc.penalties)
addqtl
E o QTL on cromossomo 7? Se quisermos verificar se há mais QTL a ser adicionado ao modelo, podemos usar a função :addqtl()
population_Z006.add <- addqtl(population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref, formula = y ~
Q1 + Q2 + Q3 + Q4 + Q5)
max(population_Z006.add)## chr pos lod
## PZA03070.9 3 54.1 3.21O LOD > 3 parece grande o suficiente para incluir essa posição, já que o limite para adicionar um efeito principal av1= 3,06 (LOD “leve” para efeitos principais). Então, vamos adicioná-lo ao modelo.
chr <- c(1, 1, 4, 7, 10)
pos <- c(24, 192, 113, 77, 68.0)
population_Z006.qtl2 <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.qtl2## QTL object containing imputed genotypes, with 16 imputations.
##
## name chr pos n.gen
## Q1 1@24.0 1 24 2
## Q2 1@192.0 1 192 2
## Q3 4@113.0 4 113 2
## Q4 7@77.0 7 77 2
## Q5 10@68.0 10 68 2plot(population_Z006.qtl2)
Lembre-se que quando um QTL é adicionado, pode-se precisar refinar posições novamente usando a função: refineqtl()
population_Z006.ref2 <- refineqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl2,
formula = y ~ Q1 + Q2 + Q3 + Q4 + Q5)## pos: 24 192 113 77 68
## Iteration 1
## Q5 pos: 68 -> 67.67358
## LOD increase: 0.181
## Q3 pos: 113 -> 114
## LOD increase: 0.276
## Q4 pos: 77 -> 70
## LOD increase: 0.862
## Q2 pos: 192 -> 132
## LOD increase: 0.934
## Q1 pos: 24 -> 24
## LOD increase: 0
## all pos: 24 192 113 77 68 -> 24 132 114 70 67.67358
## LOD increase at this iteration: 2.254
## Iteration 2
## Q1 pos: 24 -> 24
## LOD increase: 0
## Q4 pos: 70 -> 71
## LOD increase: 0.047
## Q3 pos: 114 -> 114
## LOD increase: 0
## Q5 pos: 67.67358 -> 67
## LOD increase: 0.114
## Q2 pos: 132 -> 132
## LOD increase: 0
## all pos: 24 132 114 70 67.67358 -> 24 132 114 71 67
## LOD increase at this iteration: 0.161
## Iteration 3
## Q2 pos: 132 -> 132
## LOD increase: 0
## Q4 pos: 71 -> 71
## LOD increase: 0
## Q1 pos: 24 -> 11.56667
## LOD increase: 0.505
## Q5 pos: 67 -> 66
## LOD increase: 0.132
## Q3 pos: 114 -> 112
## LOD increase: 1.002
## all pos: 24 132 114 71 67 -> 11.56667 132 112 71 66
## LOD increase at this iteration: 1.639
## Iteration 4
## Q3 pos: 112 -> 112
## LOD increase: 0
## Q5 pos: 66 -> 66
## LOD increase: 0
## Q2 pos: 132 -> 134
## LOD increase: 0.01
## Q1 pos: 11.56667 -> 11.56667
## LOD increase: 0
## Q4 pos: 71 -> 71
## LOD increase: 0
## all pos: 11.56667 132 112 71 66 -> 11.56667 134 112 71 66
## LOD increase at this iteration: 0.01
## Iteration 5
## Q4 pos: 71 -> 71
## LOD increase: 0
## Q3 pos: 112 -> 112
## LOD increase: 0
## Q1 pos: 11.56667 -> 11.56667
## LOD increase: 0
## Q5 pos: 66 -> 66
## LOD increase: 0
## Q2 pos: 134 -> 134
## LOD increase: 0
## all pos: 11.56667 134 112 71 66 -> 11.56667 134 112 71 66
## LOD increase at this iteration: 0
## overall pos: 24 192 113 77 68 -> 11.56667 134 112 71 66
## LOD increase overall: 4.064plotLodProfile(population_Z006.ref2)
population_Z006.add2 <- addqtl(population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref2, formula = y ~
Q1 + Q2 + Q3 + Q4 + Q5)
max(population_Z006.add2)## chr pos lod
## c3.loc38 3 38 4.25Porque o LOD é inferior ao nosso limiar de 3.06, então paramos aqui e podemos tentar adicionar efeitos epistáticos em seguida.
Adicionando efeitos principais automaticamente
stepwiseqtl
As penalidades para as pontuações de LOD penalizadas agora podem ser usadas por: stepwiseqtl
population_Z006.step <- stepwiseqtl(population_Z006.mds, pheno.col = "PlantHeight", max.qtl = 6, method = "hk",
penalties = penalties)## -Initial scan
## initial lod: 5.289028
## ** new best ** (pLOD increased by 2.2513)
## no.qtl = 1 pLOD = 2.251334 formula: y ~ Q1
## -Step 1
## ---Scanning for additive qtl
## plod = 5.593692
## ---Scanning for QTL interacting with Q1
## plod = 4.613319
## ---Refining positions
## no.qtl = 2 pLOD = 5.593692 formula: y ~ Q1 + Q2
## ** new best ** (pLOD increased by 3.3424)
## -Step 2
## ---Scanning for additive qtl
## plod = 7.908522
## ---Scanning for QTL interacting with Q1
## plod = 6.119383
## ---Scanning for QTL interacting with Q2
## plod = 6.113402
## ---Look for additional interactions
## plod = 4.133686
## ---Refining positions
## no.qtl = 3 pLOD = 7.908522 formula: y ~ Q1 + Q2 + Q3
## ** new best ** (pLOD increased by 2.3148)
## -Step 3
## ---Scanning for additive qtl
## plod = 8.835737
## ---Scanning for QTL interacting with Q1
## plod = 7.685776
## ---Scanning for QTL interacting with Q2
## plod = 7.042183
## ---Scanning for QTL interacting with Q3
## plod = 7.051611
## ---Look for additional interactions
## plod = 6.547946
## ---Refining positions
## --- Moved a bit
## no.qtl = 4 pLOD = 9.040116 formula: y ~ Q1 + Q2 + Q3 + Q4
## ** new best ** (pLOD increased by 1.1316)
## -Step 4
## ---Scanning for additive qtl
## plod = 9.573872
## ---Scanning for QTL interacting with Q1
## plod = 7.815094
## ---Scanning for QTL interacting with Q2
## plod = 8.069141
## ---Scanning for QTL interacting with Q3
## plod = 7.878503
## ---Scanning for QTL interacting with Q4
## plod = 7.78588
## ---Look for additional interactions
## plod = 7.828348
## ---Refining positions
## --- Moved a bit
## no.qtl = 5 pLOD = 9.642027 formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5
## ** new best ** (pLOD increased by 0.6019)
## -Step 5
## ---Scanning for additive qtl
## plod = 11.02829
## ---Scanning for QTL interacting with Q1
## plod = 9.730044
## ---Scanning for QTL interacting with Q2
## plod = 9.274608
## ---Scanning for QTL interacting with Q3
## plod = 9.682936
## ---Scanning for QTL interacting with Q4
## plod = 9.455829
## ---Scanning for QTL interacting with Q5
## plod = 9.440745
## ---Look for additional interactions
## plod = 8.572873
## ---Refining positions
## no.qtl = 6 pLOD = 11.02829 formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q6
## ** new best ** (pLOD increased by 1.3863)
## -Starting backward deletion
## ---Dropping Q6
## no.qtl = 5 pLOD = 9.642027 formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5
## ---Refining positions
## ---Dropping Q5
## no.qtl = 4 pLOD = 9.038169 formula: y ~ Q1 + Q2 + Q3 + Q4
## ---Refining positions
## --- Moved a bit
## ---Dropping Q4
## no.qtl = 3 pLOD = 7.675221 formula: y ~ Q1 + Q2 + Q3
## ---Refining positions
## --- Moved a bit
## ---Dropping Q3
## no.qtl = 2 pLOD = 4.464548 formula: y ~ Q1 + Q2
## ---Refining positions
## --- Moved a bit
## ---Dropping Q2
## no.qtl = 1 pLOD = 2.251334 formula: y ~ Q1
## ---Refining positions
## ---One last pass through refineqtlpopulation_Z006.step## QTL object containing genotype probabilities.
##
## name chr pos n.gen
## Q1 1@24.0 1 24.000 2
## Q2 3@55.0 3 55.011 2
## Q3 4@113.0 4 113.000 2
## Q4 7@82.0 7 82.000 2
## Q5 9@85.9 9 85.864 2
## Q6 10@64.3 10 64.274 2
##
## Formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q6
##
## pLOD: 11.028Adicionando epistasia
Na corrida acima, já testamos interações entre QTL no modelo (e não foram encontradas interações). stepwiseqtl
addint
No caso de nossa pesquisa manual, a QTL interativa ainda não foi testada. Uma maneira de procurar explicitamente a epistasis é usando a função, por isso usamos nosso objeto: addint() population_Z006.ref
addint(population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref2, formula = y ~ Q1 +
Q2 + Q3 + Q4 +Q5)## Method: multiple imputation
## Model: normal phenotype
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5
##
## Add one pairwise interaction at a time table:
## --------------------------------------------
## df Type III SS LOD %var F value Pvalue(Chi2)
## 1@11.6:1@134.0 1 30.37 0.04908 0.06855 0.21759 0.634
## 1@11.6:4@112.0 1 11.63 0.01879 0.02626 0.08329 0.769
## 1@11.6:7@71.0 1 207.42 0.33643 0.46821 1.49698 0.213
## 1@11.6:10@66.0 1 -14.98 -0.02418 -0.03381 -0.10712 1.000
## 1@134.0:4@112.0 1 -23.76 -0.03837 -0.05364 -0.16992 1.000
## 1@134.0:7@71.0 1 438.96 0.71537 0.99089 3.19813 0.070
## 1@134.0:10@66.0 1 386.89 0.62984 0.87335 2.81277 0.089
## 4@112.0:7@71.0 1 401.16 0.65326 0.90556 2.91821 0.083
## 4@112.0:10@66.0 1 66.95 0.10828 0.15112 0.48045 0.480
## 7@71.0:10@66.0 1 494.16 0.80623 1.11549 3.60844 0.054
## Pvalue(F)
## 1@11.6:1@134.0 0.6414
## 1@11.6:4@112.0 0.7732
## 1@11.6:7@71.0 0.2227
## 1@11.6:10@66.0 1.0000
## 1@134.0:4@112.0 1.0000
## 1@134.0:7@71.0 0.0754 .
## 1@134.0:10@66.0 0.0952 .
## 4@112.0:7@71.0 0.0893 .
## 4@112.0:10@66.0 0.4891
## 7@71.0:10@66.0 0.0591 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Como todos os LODs estão abaixo de 4,21 ( limiar de LOD de nossas permutações acima), determinamos que não há evidência de epistasis entre os QTL.
scantwo
Olhar para interações epistáticas entre QTL com efeitos principais é muito limitador. Então, podemos usar a função para procurar evidências de interações por todo o genoma. Além disso, devemos ser capazes de separar pares de QTL ligados com tal função.scantwo()
Por se trata de uma pesquisa muito computacionalmente intensiva, usamos para acelerar um pouco o processo: step = 2
population_Z006.mds <- calc.genoprob(population_Z006.mds, step = 2)
population_Z006.two <- scantwo(population_Z006.mds, pheno.col = "PlantHeight", method = "hk")## --Running scanone
## --Running scantwo
## (1,1)
## (1,2)
## (1,3)
## (1,4)
## (1,5)
## (1,6)
## (1,7)
## (1,8)
## (1,9)
## (1,10)
## (2,2)
## (2,3)
## (2,4)
## (2,5)
## (2,6)
## (2,7)
## (2,8)
## (2,9)
## (2,10)
## (3,3)
## (3,4)
## (3,5)
## (3,6)
## (3,7)
## (3,8)
## (3,9)
## (3,10)
## (4,4)
## (4,5)
## (4,6)
## (4,7)
## (4,8)
## (4,9)
## (4,10)
## (5,5)
## (5,6)
## (5,7)
## (5,8)
## (5,9)
## (5,10)
## (6,6)
## (6,7)
## (6,8)
## (6,9)
## (6,10)
## (7,7)
## (7,8)
## (7,9)
## (7,10)
## (8,8)
## (8,9)
## (8,10)
## (9,9)
## (9,10)
## (10,10)save(population_Z006.two, file = "population_Z006_two.RData")load("population_Z006_two.RData")
plot(population_Z006.two, col.scheme = "redblue")
summary(population_Z006.two)## pos1f pos2f lod.full lod.fv1 lod.int pos1a pos2a lod.add lod.av1
## c1 :c1 158 190 4.50 1.760 0.47565 26 156 4.024 1.284
## c1 :c2 156 70 4.26 1.518 0.74081 24 122 3.517 0.778
## c1 :c3 24 34 6.49 2.611 0.07160 24 36 6.414 2.539
## c1 :c4 26 112 6.82 3.526 0.87538 24 112 5.943 2.651
## c1 :c5 22 10 5.50 2.764 0.36089 24 34 5.142 2.403
## c1 :c6 26 118 3.54 0.799 0.18695 24 62 3.352 0.612
## c1 :c7 24 70 8.52 3.381 0.00141 24 70 8.523 3.380
## c1 :c8 22 102 5.88 3.141 2.46038 24 112 3.420 0.680
## c1 :c9 2 86 6.30 3.559 0.36185 26 86 5.937 3.197
## c1 :c10 22 68 9.37 4.117 0.81979 22 68 8.545 3.297
## c2 :c2 56 60 2.28 1.789 0.00965 56 60 2.268 1.779
## c2 :c3 32 34 5.69 1.813 1.27530 116 36 4.413 0.538
## c2 :c4 82 112 4.44 1.144 0.76953 116 112 3.667 0.375
## c2 :c5 68 8 3.41 1.453 1.00047 114 34 2.409 0.452
## c2 :c6 54 102 1.24 0.753 0.25391 116 62 0.989 0.499
## c2 :c7 64 86 7.51 2.367 1.27071 116 82 6.239 1.096
## c2 :c8 112 114 2.22 1.454 0.96338 116 38 1.259 0.491
## c2 :c9 50 80 3.32 0.875 0.53653 54 86 2.780 0.338
## c2 :c10 86 68 6.53 1.286 0.82034 108 68 5.715 0.466
## c3 :c3 36 126 5.14 1.270 0.44470 36 134 4.700 0.825
## c3 :c4 34 112 7.39 3.516 0.01971 34 112 7.371 3.496
## c3 :c5 36 8 7.82 3.950 2.20550 36 20 5.619 1.744
## c3 :c6 36 0 5.36 1.483 0.93234 36 62 4.426 0.551
## c3 :c7 36 82 8.93 3.790 0.02097 36 82 8.912 3.769
## c3 :c8 36 38 5.22 1.344 0.33983 36 110 4.879 1.004
## c3 :c9 36 80 6.80 2.921 0.03871 36 80 6.758 2.882
## c3 :c10 36 68 9.02 3.774 0.00807 36 68 9.014 3.766
## c4 :c4 6 112 4.79 1.499 0.12532 60 112 4.666 1.374
## c4 :c5 112 26 5.72 2.427 0.49356 112 34 5.226 1.934
## c4 :c6 112 48 4.64 1.351 0.78855 112 8 3.855 0.563
## c4 :c7 116 82 10.06 4.915 0.08615 116 78 9.971 4.828
## c4 :c8 112 82 6.12 2.833 1.89842 112 80 4.227 0.934
## c4 :c9 112 86 6.65 3.361 0.07639 112 86 6.577 3.284
## c4 :c10 118 98 9.04 3.796 0.35144 112 62 8.693 3.445
## c5 :c5 34 120 3.56 1.602 0.01141 34 120 3.547 1.591
## c5 :c6 2 48 3.69 1.737 1.32917 34 62 2.364 0.408
## c5 :c7 6 82 8.72 3.574 1.36185 12 60 7.356 2.213
## c5 :c8 20 110 3.55 1.595 0.63507 34 110 2.916 0.960
## c5 :c9 10 86 5.49 3.047 0.78199 34 86 4.707 2.265
## c5 :c10 48 68 8.35 3.104 1.11136 34 68 7.241 1.993
## c6 :c6 62 64 3.10 2.633 1.90892 48 50 1.193 0.724
## c6 :c7 0 60 6.48 1.340 0.73159 62 70 5.751 0.608
## c6 :c8 6 38 1.93 1.159 0.73411 62 110 1.193 0.425
## c6 :c9 40 84 3.00 0.559 0.01172 62 86 2.989 0.547
## c6 :c10 78 68 6.64 1.393 0.89420 112 68 5.748 0.499
## c7 :c7 60 86 7.60 2.457 0.04731 60 82 7.553 2.410
## c7 :c8 88 110 7.21 2.071 0.82849 82 110 6.386 1.243
## c7 :c9 72 78 8.62 3.475 1.17323 82 86 7.445 2.302
## c7 :c10 72 68 12.34 7.092 0.85658 78 68 11.484 6.235
## c8 :c8 38 60 2.96 2.192 1.36403 38 110 1.597 0.828
## c8 :c9 110 86 3.36 0.917 0.13600 110 86 3.223 0.781
## c8 :c10 4 68 6.11 0.866 0.35597 38 68 5.759 0.510
## c9 :c9 4 86 3.98 1.538 1.14256 62 86 2.837 0.396
## c9 :c10 86 68 7.35 2.101 0.04101 86 68 7.308 2.060
## c10:c10 68 108 7.07 1.825 0.09247 68 108 6.981 1.733A partir do gráfico e resumo acima, não há interações entre os loci nos cromossomos.
chr <- c(1, 1, 4, 7, 10)
pos <- c(24, 192, 113, 77, 68.0)
population_Z006.qtl3 <- makeqtl(cross = population_Z006.mds, chr = chr, pos = pos)
population_Z006.qtl3## QTL object containing imputed genotypes, with 16 imputations.
##
## name chr pos n.gen
## Q1 1@24.0 1 24 2
## Q2 1@192.0 1 192 2
## Q3 4@113.0 4 113 2
## Q4 7@77.0 7 77 2
## Q5 10@68.0 10 68 2population_Z006.ref3 <- refineqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.qtl3,
formula = y ~ Q1 + Q2 + Q3 + Q4 +Q5)## pos: 24 192 113 77 68
## Iteration 1
## Q4 pos: 77 -> 70
## LOD increase: 1.194
## Q5 pos: 68 -> 67.67358
## LOD increase: 0.194
## Q2 pos: 192 -> 132
## LOD increase: 1.015
## Q3 pos: 113 -> 113
## LOD increase: 0
## Q1 pos: 24 -> 11.56667
## LOD increase: 0.13
## all pos: 24 192 113 77 68 -> 11.56667 132 113 70 67.67358
## LOD increase at this iteration: 2.533
## Iteration 2
## Q1 pos: 11.56667 -> 11.56667
## LOD increase: 0
## Q2 pos: 132 -> 132
## LOD increase: 0
## Q4 pos: 70 -> 69
## LOD increase: 0.232
## Q3 pos: 113 -> 113
## LOD increase: 0
## Q5 pos: 67.67358 -> 66
## LOD increase: 0.731
## all pos: 11.56667 132 113 70 67.67358 -> 11.56667 132 113 69 66
## LOD increase at this iteration: 0.963
## Iteration 3
## Q5 pos: 66 -> 66
## LOD increase: 0
## Q2 pos: 132 -> 133
## LOD increase: 0.199
## Q3 pos: 113 -> 113
## LOD increase: 0
## Q1 pos: 11.56667 -> 11.56667
## LOD increase: 0
## Q4 pos: 69 -> 71
## LOD increase: 0.074
## all pos: 11.56667 132 113 69 66 -> 11.56667 133 113 71 66
## LOD increase at this iteration: 0.272
## Iteration 4
## Q4 pos: 71 -> 71
## LOD increase: 0
## Q1 pos: 11.56667 -> 11.56667
## LOD increase: 0
## Q5 pos: 66 -> 66
## LOD increase: 0
## Q3 pos: 113 -> 112
## LOD increase: 0.254
## Q2 pos: 133 -> 134
## LOD increase: 0.041
## all pos: 11.56667 133 113 71 66 -> 11.56667 134 112 71 66
## LOD increase at this iteration: 0.296
## Iteration 5
## Q2 pos: 134 -> 134
## LOD increase: 0
## Q4 pos: 71 -> 71
## LOD increase: 0
## Q5 pos: 66 -> 66
## LOD increase: 0
## Q1 pos: 11.56667 -> 11.56667
## LOD increase: 0
## Q3 pos: 112 -> 112
## LOD increase: 0
## all pos: 11.56667 134 112 71 66 -> 11.56667 134 112 71 66
## LOD increase at this iteration: 0
## overall pos: 24 192 113 77 68 -> 11.56667 134 112 71 66
## LOD increase overall: 4.064summary(population_Z006.ref3)## QTL object containing imputed genotypes, with 16 imputations.
##
## name chr pos n.gen
## Q1 1@11.6 1 11.567 2
## Q2 1@134.0 1 134.000 2
## Q3 4@112.0 4 112.000 2
## Q4 7@71.0 7 71.000 2
## Q5 10@66.0 10 66.000 2plotLodProfile(population_Z006.ref3)
population_Z006.fit3 <- fitqtl(cross = population_Z006.mds, pheno.col = "PlantHeight", qtl = population_Z006.ref3,
formula = y ~ Q1 + Q2 + Q3 + Q4 +Q5, get.ests = TRUE)
summary(population_Z006.fit3)##
## fitqtl summary
##
## Method: multiple imputation
## Model: normal phenotype
## Number of observations : 185
##
## Full model result
## ----------------------------------
## Model formula: y ~ Q1 + Q2 + Q3 + Q4 + Q5
##
## df SS MS LOD %var Pvalue(Chi2) Pvalue(F)
## Model 5 19429.35 3885.8709 23.19139 43.85873 0 0
## Error 179 24870.50 138.9414
## Total 184 44299.86
##
##
## Drop one QTL at a time ANOVA table:
## ----------------------------------
## df Type III SS LOD %var F value Pvalue(Chi2) Pvalue(F)
## 1@11.6 1 4276 6.373 9.652 30.773 0.000 1.03e-07 ***
## 1@134.0 1 1271 2.002 2.868 9.145 0.002 0.00286 **
## 4@112.0 1 4863 7.175 10.978 35.001 0.000 1.64e-08 ***
## 7@71.0 1 7785 10.940 17.573 56.028 0.000 3.12e-12 ***
## 10@66.0 1 7231 10.253 16.322 52.042 0.000 1.48e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Estimated effects:
## -----------------
## est SE t
## Intercept 147.6777 0.8948 165.048
## 1@11.6 -4.6729 0.9603 -4.866
## 1@134.0 -2.1688 0.9187 -2.361
## 4@112.0 -4.5862 0.8918 -5.142
## 7@71.0 6.2045 0.8921 6.955
## 10@66.0 5.7977 0.8949 6.478Federal Universidade Federal de Viçosa, helio.d.junior@ufv.br↩︎